diff --git a/AGENTS.md b/AGENTS.md
index 53f5d916ea0086c1b5f7282a911ffe5d0fcd0026..d2663f654983ecd64642835be8deb634c89258b3 100644
--- a/AGENTS.md
+++ b/AGENTS.md
@@ -6,13 +6,15 @@ run no compute, so they are free; do not switch it to Gradio or Docker (those ne
 
 ## Files
 
-- `index.html`: the whole page: header, the four tab panels (Leaderboard, Per dataset,
-  Stability & validity, About), all user-facing text, the citation, `<meta name="selectarena-version">`
+- `index.html`: the whole page: header, the five tab panels (Performance, Stability, Validity,
+  Data overview, About), all user-facing text, the citation, `<meta name="selectarena-version">`
   and the version history (`<ul id="version-history">`, newest first).
 - `assets/app.js`: plain JavaScript, no build step. Fetches `data/meta.json`, renders the selector
-  chips (task type, downstream model, budget; combinations that are not published are disabled),
-  sets the iframes' `src` to the generated pages and resizes them when a page posts
-  `{saFrameHeight}`. Keeps the selection in the URL hash.
+  chips (task type, downstream model, budget; combinations that are not published are disabled;
+  task type, view and noise level of the lasagna plots), sets the iframes' `src` and the figures'
+  `<img src>` to the generated files and resizes the iframes when a page posts `{saFrameHeight}`.
+  Keeps the tab and the selections in the URL hash (old hashes `#leaderboard`, `#robustness` and
+  `#datasets` still work).
 - `assets/style.css`: color tokens on `:root` (light) and in `prefers-color-scheme: dark`.
 - `smoke_test.py`: standard-library check of `data/` and of the version (see below).
 
@@ -21,10 +23,16 @@ run no compute, so they are free; do not switch it to Gradio or Docker (those ne
 `data/` is SelectArena's `generated_website_artifacts/clean_website_artifacts/website_data/`:
 
 - `meta.json`: counts, `models` (key -> name), `budgets`, `total_budget`, `task_types`,
-  `has_stability`, `has_validity`, `validity_noise`, `validity_tables`, `generated_at`, and `subsets` (each with
-  `tasks`, `model`, `budget`, `path`, `n_methods`, `n_datasets`, `n_comparisons`).
+  `has_stability`, `has_validity`, `validity_noise`, `validity_tables`, `generated_at`, `subsets` (each with
+  `tasks`, `model`, `budget`, `path`, `n_methods`, `n_datasets`, `n_comparisons`, `pareto`),
+  `lasagna` (`variants`, `variant_labels`, and when present `stability` = `{path, task_types}` and
+  `validity` = a list of `{noise, path, task_types}`) and `has_results_explorer`.
 - `subsets/tasks_<t>/models_<m>/budgets_<b>/`: `website_leaderboard.csv`, `leaderboard_table.html`,
-  `elo_explorer.html`.
+  `elo_explorer.html`, and `pareto.svg` / `pareto.csv` when the subset's `pareto` is true.
+- `lasagna/stability/tasks_<t>/` and `lasagna/validity/noise_<level>/tasks_<t>/`: one
+  `<variant>.svg` per entry of `lasagna.variants` and `per_epv.csv` (the plotted values).
+- `results.csv` and `results_explorer.html`: the results per dataset, method, downstream model and
+  budget (Data overview tab).
 - `per_dataset.csv`, `per_dataset_table.html` (datasets by name), and optionally `stability.csv`,
   `stability_table.html`, `validity.csv` and one `validity_noise_<level>_table.html` per noise level
   (listed in `meta.json` as `validity_tables`).
@@ -32,8 +40,11 @@ run no compute, so they are free; do not switch it to Gradio or Docker (those ne
   `Subgroup` (the paper's taxonomy, e.g. Tree, Backward search), Elo and its interval, the metrics.
 
 The generated pages are self-contained (inline CSS/JS, no network) and post their height to the
-parent window. `subsetDir()` in `app.js` mirrors `selectarena.website.subsets.Subset.rel_path`;
-change both together.
+parent window. The SVG figures (Pareto front, lasagna plots) are matplotlib output shown with
+`<img>` on a white card, as in the paper; they do not follow dark mode.
+
+`subsetDir()` in `app.js` mirrors `selectarena.website.subsets.Subset.rel_path`; change both
+together.
 
 ## Conventions
 
@@ -44,5 +55,5 @@ change both together.
   (`<li><strong>YYYY/MM/DD · vX.Y.Z</strong>: ...</li>`). `smoke_test.py` fails if they differ.
 - No external scripts, fonts or CDNs; everything is served from this repo.
 - Verification: `python smoke_test.py`, then `python -m http.server 8000` and a look at
-  http://localhost:8000 (all four tabs, a few selections, light and dark mode).
+  http://localhost:8000 (all five tabs, a few selections, light and dark mode).
 - Do not commit or push without an explicit request: pushing deploys the Space.
diff --git a/assets/app.js b/assets/app.js
index c3a51110f363c24c769f02d9538aa650acd97d80..b2a11474278a2781f0338a6975ff6715d491b5e0 100644
--- a/assets/app.js
+++ b/assets/app.js
@@ -2,7 +2,9 @@
 //
 // data/meta.json lists every published subset; each subset folder
 // data/subsets/tasks_<t>/models_<m>/budgets_<b>/ holds elo_explorer.html and leaderboard_table.html,
-// which are embedded as iframes. The folder names mirror selectarena.website.subsets.Subset.rel_path.
+// which are embedded as iframes, and pareto.svg / pareto.csv. The folder names mirror
+// selectarena.website.subsets.Subset.rel_path. The lasagna plots are in data/lasagna/ (meta.lasagna):
+// stability/tasks_<t>/<variant>.svg and validity/noise_<level>/tasks_<t>/<variant>.svg.
 // Every embedded page posts {saFrameHeight: <px>} to this window; the listener below resizes its iframe.
 (function () {
   'use strict';
@@ -15,8 +17,19 @@
     multiclass: 'Multiclass',
     regression: 'Regression'
   };
-  var TABS = ['leaderboard', 'datasets', 'robustness', 'about'];
-  var state = { tab: 'leaderboard', tasks: ALL, model: ALL, budget: ALL };
+  var DEFAULT_VARIANT_LABELS = {
+    plain: 'Heat map',
+    overlay_top_3: 'Top-3 outlined',
+    binary_thr_003: 'Within 0.03 of the best'
+  };
+  var TABS = ['performance', 'stability', 'validity', 'data', 'about'];
+  // Old links (#tab=leaderboard etc.) still open the matching tab.
+  var TAB_ALIASES = { leaderboard: 'performance', robustness: 'stability', datasets: 'data' };
+  var DEFAULTS = {
+    tab: 'performance', tasks: ALL, model: ALL, budget: ALL,
+    stasks: ALL, svariant: 'plain', vtasks: ALL, vvariant: 'plain', noise: ''
+  };
+  var state = Object.assign({}, DEFAULTS);
   var meta = null;
   var published = {};
 
@@ -33,6 +46,8 @@
 
   // 0.5 -> "0.5", 0.75 -> "0.75", 1 -> "1.0"
   function noiseLabel(value) { return Number(value).toFixed(2).replace(/0$/, ''); }
+  // Folder name of a noise level, as Python's f"{noise:g}": 0.5 -> "0.5", 1 -> "1"
+  function noiseKey(value) { return String(Number(value)); }
 
   function budgetLabel(index, total) {
     var label = 'Budget ' + (index + 1);
@@ -41,29 +56,34 @@
     return label;
   }
 
-  // ---- URL hash: #tab=...&tasks=...&model=...&budget=... (shareable selections) ----
+  // ---- URL hash: #tab=...&tasks=...&model=... (shareable selections) ----
   function readHash() {
     var params = new URLSearchParams(window.location.hash.slice(1));
-    ['tab', 'tasks', 'model', 'budget'].forEach(function (k) {
+    Object.keys(DEFAULTS).forEach(function (k) {
       var v = params.get(k);
       if (v) state[k] = v;
     });
-    if (TABS.indexOf(state.tab) < 0) state.tab = 'leaderboard';
+    state.tab = TAB_ALIASES[state.tab] || state.tab;
+    if (TABS.indexOf(state.tab) < 0) state.tab = DEFAULTS.tab;
   }
   function writeHash() {
     var params = new URLSearchParams();
-    if (state.tab !== 'leaderboard') params.set('tab', state.tab);
-    ['tasks', 'model', 'budget'].forEach(function (k) { if (state[k] !== ALL) params.set(k, state[k]); });
+    Object.keys(DEFAULTS).forEach(function (k) {
+      if (state[k] && state[k] !== DEFAULTS[k]) params.set(k, state[k]);
+    });
     var hash = params.toString();
     try {
       history.replaceState(null, '', hash ? '#' + hash : window.location.pathname + window.location.search);
     } catch (e) { /* sandboxed frame: the selection still works, it just is not in the URL */ }
   }
 
-  // ---- iframes ----
+  // ---- iframes and images ----
   function setFrame(frame, src) {
     if (frame.getAttribute('src') !== src) frame.setAttribute('src', src);
   }
+  function setImage(img, src) {
+    if (img.getAttribute('src') !== src) img.setAttribute('src', src);
+  }
   window.addEventListener('message', function (event) {
     var data = event.data;
     if (!data || typeof data.saFrameHeight !== 'number') return;
@@ -72,6 +92,25 @@
     });
   });
 
+  // A row of chips for one choice. `options` are {value, label, disabled?}; `onPick` gets the value.
+  function renderChips(containerId, options, selected, onPick) {
+    var container = $(containerId);
+    container.textContent = '';
+    options.forEach(function (option) {
+      var button = document.createElement('button');
+      button.type = 'button';
+      button.className = 'chip';
+      button.textContent = option.label;
+      button.setAttribute('aria-pressed', String(option.value === selected));
+      if (option.disabled && option.value !== selected) {
+        button.disabled = true;
+        button.title = option.disabledTitle || 'Not available for this selection';
+      }
+      button.addEventListener('click', function () { onPick(option.value); writeHash(); });
+      container.appendChild(button);
+    });
+  }
+
   // ---- header ----
   function renderFacts() {
     var version = document.querySelector('meta[name="selectarena-version"]').getAttribute('content');
@@ -101,8 +140,9 @@
       $('tab-' + t).tabIndex = t === tab ? 0 : -1;
       $('panel-' + t).hidden = t !== tab;
     });
-    if (tab === 'datasets') loadDatasets();
-    if (tab === 'robustness') loadRobustness();
+    if (tab === 'stability') renderStability();
+    if (tab === 'validity') renderValidity();
+    if (tab === 'data') loadData();
     writeHash();
   }
   function initTabs() {
@@ -119,7 +159,7 @@
     });
   }
 
-  // ---- leaderboard ----
+  // ---- performance ----
   function axisOptions() {
     var taskTypes = meta.task_types || [ALL];
     var models = meta.models || {};
@@ -134,32 +174,17 @@
     };
   }
 
-  function renderChips(axis, options) {
-    var container = $('chips-' + axis);
-    container.textContent = '';
-    options.forEach(function (option) {
-      var button = document.createElement('button');
-      button.type = 'button';
-      button.className = 'chip';
-      button.textContent = option.label;
-      button.setAttribute('aria-pressed', String(option.value === state[axis]));
-      var available = published[key(withAxis(axis, option.value))];
-      if (!available && option.value !== state[axis]) {
-        button.disabled = true;
-        button.title = 'No leaderboard for this combination';
-      }
-      button.addEventListener('click', function () {
-        state[axis] = option.value;
-        renderLeaderboard();
-        writeHash();
-      });
-      container.appendChild(button);
-    });
-  }
-
-  function renderLeaderboard() {
+  function renderPerformance() {
     var options = axisOptions();
-    ['tasks', 'model', 'budget'].forEach(function (axis) { renderChips(axis, options[axis]); });
+    ['tasks', 'model', 'budget'].forEach(function (axis) {
+      renderChips('chips-' + axis, options[axis].map(function (o) {
+        return {
+          value: o.value, label: o.label,
+          disabled: !published[key(withAxis(axis, o.value))],
+          disabledTitle: 'No leaderboard for this combination'
+        };
+      }), state[axis], function (value) { state[axis] = value; renderPerformance(); });
+    });
 
     var info = published[key(state)];
     var scope = [
@@ -189,41 +214,101 @@
       table.style.height = (info.n_methods * 37 + 90) + 'px';
     }
     setFrame(table, dir + 'leaderboard_table.html');
+
+    $('pareto-missing').hidden = !!info.pareto;
+    $('pareto-figure').hidden = !info.pareto;
+    if (info.pareto) {
+      setImage($('img-pareto'), dir + 'pareto.svg');
+      $('link-pareto-svg').href = dir + 'pareto.svg';
+      $('link-pareto-csv').href = dir + 'pareto.csv';
+    }
   }
 
-  // ---- other tabs (loaded on first view) ----
-  function loadDatasets() {
-    if (!meta) { $('datasets-missing').hidden = false; $('frame-datasets').hidden = true; return; }
-    setFrame($('frame-datasets'), 'data/per_dataset_table.html');
+  // ---- lasagna plots (stability and validity) ----
+  function variantOptions() {
+    var lasagna = (meta && meta.lasagna) || {};
+    var labels = lasagna.variant_labels || DEFAULT_VARIANT_LABELS;
+    return (lasagna.variants || Object.keys(DEFAULT_VARIANT_LABELS)).map(function (v) {
+      return { value: v, label: labels[v] || v };
+    });
+  }
+  function taskOptions(available) {
+    return Object.keys(TASK_LABELS)
+      .filter(function (k) { return available.indexOf(k) >= 0; })
+      .map(function (k) { return { value: k, label: TASK_LABELS[k] }; });
+  }
+  function showLasagna(prefix, folder, tasks, variant) {
+    var dir = 'data/' + folder + '/tasks_' + tasks + '/';
+    setImage($('img-' + prefix), dir + variant + '.svg');
+    $('link-' + prefix + '-svg').href = dir + variant + '.svg';
+    $('link-' + prefix + '-csv').href = dir + 'per_epv.csv';
   }
-  function loadRobustness() {
-    var hasStability = !!(meta && meta.has_stability);
-    $('stability-missing').hidden = hasStability;
-    $('frame-stability').hidden = !hasStability;
-    if (hasStability) setFrame($('frame-stability'), 'data/stability_table.html');
 
-    // One validity table per noise level (meta.validity_tables); older data had a single table.
+  function renderStability() {
+    var info = meta && meta.lasagna && meta.lasagna.stability;
+    var hasTable = !!(meta && meta.has_stability);
+    $('stability-missing').hidden = !!(info || hasTable);
+    $('stability-content').hidden = !(info || hasTable);
+    if (hasTable) setFrame($('frame-stability'), 'data/stability_table.html');
+    var figure = $('img-stability').closest('figure');
+    figure.hidden = !info;
+    $('chips-stab-tasks').closest('.controls').hidden = !info;
+    if (!info) return;
+    if (info.task_types.indexOf(state.stasks) < 0) state.stasks = info.task_types[0];
+    renderChips('chips-stab-tasks', taskOptions(info.task_types), state.stasks,
+      function (value) { state.stasks = value; renderStability(); });
+    var variants = variantOptions();
+    if (!variants.some(function (v) { return v.value === state.svariant; })) state.svariant = variants[0].value;
+    renderChips('chips-stab-variant', variants, state.svariant,
+      function (value) { state.svariant = value; renderStability(); });
+    showLasagna('stability', info.path, state.stasks, state.svariant);
+  }
+
+  function renderValidity() {
+    var levels = (meta && meta.lasagna && meta.lasagna.validity) || [];
     var tables = (meta && meta.validity_tables) || [];
-    if (!tables.length && meta && meta.has_validity) tables = [{ noise: null, path: 'validity_table.html' }];
-    $('validity-missing').hidden = tables.length > 0;
-    var container = $('validity-tables');
-    if (!container.childElementCount) {
-      tables.forEach(function (table) {
-        if (table.noise != null) {
-          var heading = document.createElement('h3');
-          heading.textContent = 'Noise level ' + noiseLabel(table.noise) +
-            (Number(table.noise) === Number(meta.validity_noise) ? ' (used on the leaderboard)' : '');
-          container.appendChild(heading);
-        }
-        var frame = document.createElement('iframe');
-        frame.className = 'frame sa-frame';
-        frame.title = 'Validity per method' + (table.noise != null ? ' at noise level ' + noiseLabel(table.noise) : '');
-        frame.style.height = '640px';
-        frame.src = 'data/' + table.path;
-        container.appendChild(frame);
-      });
-    }
+    $('validity-missing').hidden = levels.length > 0 || tables.length > 0;
+    $('validity-content').hidden = !(levels.length || tables.length);
     if (meta && meta.validity_noise != null) $('validity-noise').textContent = noiseLabel(meta.validity_noise);
+
+    // Noise levels from the plots, or else from the tables.
+    var noises = (levels.length ? levels : tables).map(function (l) { return noiseKey(l.noise); });
+    if (!noises.length) return;
+    if (noises.indexOf(state.noise) < 0) {
+      var preferred = meta.validity_noise != null ? noiseKey(meta.validity_noise) : noises[0];
+      state.noise = noises.indexOf(preferred) >= 0 ? preferred : noises[0];
+    }
+    renderChips('chips-val-noise', noises.map(function (n) {
+      return { value: n, label: noiseLabel(n) + ' noise feature' + (Number(n) === 1 ? '' : 's') + ' per feature' };
+    }), state.noise, function (value) { state.noise = value; renderValidity(); });
+
+    var table = tables.filter(function (t) { return noiseKey(t.noise) === state.noise; })[0];
+    $('frame-validity').hidden = !table;
+    if (table) setFrame($('frame-validity'), 'data/' + table.path);
+
+    var level = levels.filter(function (l) { return noiseKey(l.noise) === state.noise; })[0];
+    $('img-validity').closest('figure').hidden = !level;
+    $('chips-val-tasks').closest('.control').hidden = !level;
+    $('chips-val-variant').closest('.control').hidden = !level;
+    if (!level) return;
+    if (level.task_types.indexOf(state.vtasks) < 0) state.vtasks = level.task_types[0];
+    renderChips('chips-val-tasks', taskOptions(level.task_types), state.vtasks,
+      function (value) { state.vtasks = value; renderValidity(); });
+    var variants = variantOptions();
+    if (!variants.some(function (v) { return v.value === state.vvariant; })) state.vvariant = variants[0].value;
+    renderChips('chips-val-variant', variants, state.vvariant,
+      function (value) { state.vvariant = value; renderValidity(); });
+    showLasagna('validity', level.path, state.vtasks, state.vvariant);
+  }
+
+  // ---- data overview ----
+  function loadData() {
+    var hasExplorer = !!(meta && meta.has_results_explorer);
+    $('data-missing').hidden = !!meta;
+    $('frame-results').hidden = !hasExplorer;
+    if (hasExplorer) setFrame($('frame-results'), 'data/results_explorer.html');
+    $('frame-datasets').hidden = !meta;
+    if (meta) setFrame($('frame-datasets'), 'data/per_dataset_table.html');
   }
 
   // ---- start ----
@@ -241,7 +326,7 @@
         if (!published[key(state)]) { state.tasks = ALL; state.model = ALL; state.budget = ALL; }
         $('leaderboard-content').hidden = false;
         renderFacts();
-        renderLeaderboard();
+        renderPerformance();
         showTab(state.tab);
       })
       .catch(function () {
diff --git a/assets/style.css b/assets/style.css
index 78c13b40394aa8076a311713bca6dadb8b1c5ef0..14e384825edc3848a5913cd13f042b26284658db 100644
--- a/assets/style.css
+++ b/assets/style.css
@@ -119,3 +119,13 @@ pre {
 }
 .history { padding-left: 18px; }
 .history li { margin: 6px 0; }
+
+/* Figures rendered by matplotlib (Pareto front, lasagna plots): drawn on white, so they sit on a
+   white card in dark mode too. */
+.plot { margin: 4px 0 22px; }
+.plot img {
+  display: block; width: 100%; max-width: 900px; height: auto;
+  background: #ffffff; border: 1px solid var(--line); border-radius: var(--radius); padding: 8px;
+}
+.plot[hidden] { display: none; }
+.plot figcaption { margin-top: 8px; }
diff --git a/data/lasagna/stability/tasks_all/binary_thr_003.svg b/data/lasagna/stability/tasks_all/binary_thr_003.svg
new file mode 100644
index 0000000000000000000000000000000000000000..01696b7bc9477a025475961c1ef2ed99f133dd1f
--- /dev/null
+++ b/data/lasagna/stability/tasks_all/binary_thr_003.svg
@@ -0,0 +1,327 @@
+<?xml version="1.0" encoding="utf-8" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
+  "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<svg xmlns:xlink="http://www.w3.org/1999/xlink" width="568.856562pt" height="272.79625pt" viewBox="0 0 568.856562 272.79625" xmlns="http://www.w3.org/2000/svg" version="1.1">
+ <metadata>
+  <rdf:RDF xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://creativecommons.org/ns#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
+   <cc:Work>
+    <dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage"/>
+    <dc:format>image/svg+xml</dc:format>
+    <dc:creator>
+     <cc:Agent>
+      <dc:title>Matplotlib v3.10.1, https://matplotlib.org/</dc:title>
+     </cc:Agent>
+    </dc:creator>
+   </cc:Work>
+  </rdf:RDF>
+ </metadata>
+ <defs>
+  <style type="text/css">*{stroke-linejoin: round; stroke-linecap: butt}</style>
+ </defs>
+ <g id="figure_1">
+  <g id="patch_1">
+   <path d="M 0 272.79625 
+L 568.856562 272.79625 
+L 568.856562 0 
+L 0 0 
+z
+" style="fill: #ffffff"/>
+  </g>
+  <g id="axes_1">
+   <g id="patch_2">
+    <path d="M 89.901563 235.24 
+L 561.656562 235.24 
+L 561.656562 7.2 
+L 89.901563 7.2 
+z
+" style="fill: #ffffff"/>
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index 0000000000000000000000000000000000000000..3494ecc84aca30cda41f702e8219078d37073796
--- /dev/null
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diff --git a/data/lasagna/stability/tasks_binary/per_epv.csv b/data/lasagna/stability/tasks_binary/per_epv.csv
new file mode 100644
index 0000000000000000000000000000000000000000..d9c336b4cc668499123eede156d77fa4b13496e1
--- /dev/null
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new file mode 100644
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new file mode 100644
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--- /dev/null
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+MI,541.4167,0.8474
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+RFImportance,43.0833,0.7711
+RFImportance,541.4167,0.821
+RFImportance,891.0,0.8072
+RFImportance,918.4,0.845
+RFImportance,1448.5923,0.9436
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+Random,8.5417,-0.0037
+Random,19.3846,0.0058
+Random,43.0833,0.0033
+Random,259.3049,0.0033
+Random,302.086,-0.0005
+Random,541.4167,0.0068
+Random,891.0,0.003
+Random,918.4,-0.0044
+Random,1448.5923,-0.0046
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+SBE,43.0833,0.171
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+SBE,302.086,-0.0
+SBE,541.4167,0.2621
+SBE,891.0,0.145
+SBE,918.4,0.472
+SBE,1448.5923,0.0003
+SFS,5.6064,0.2906
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+SFS,19.3846,0.1247
+SFS,43.0833,0.0307
+SFS,259.3049,0.1318
+SFS,541.4167,0.361
+SFS,891.0,0.0832
+SFS,918.4,0.6152
+SFS,1448.5923,0.652
+mRMR,5.6064,-0.0007
+mRMR,8.5417,0.4668
+mRMR,19.3846,0.2847
+mRMR,43.0833,0.2375
+mRMR,541.4167,0.6217
+mRMR,891.0,0.9265
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new file mode 100644
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index 0000000000000000000000000000000000000000..0bde9c6fd97b1a5120c42d4c758302ecb6927ed3
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diff --git a/data/lasagna/validity/noise_0.5/tasks_binary/per_epv.csv b/data/lasagna/validity/noise_0.5/tasks_binary/per_epv.csv
new file mode 100644
index 0000000000000000000000000000000000000000..ad2963ba092b7062956c3c1c60c263ba6b710f52
--- /dev/null
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new file mode 100644
index 0000000000000000000000000000000000000000..3b1687803467916e492fabf262ae9b4cb1a989ba
--- /dev/null
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new file mode 100644
index 0000000000000000000000000000000000000000..fb7f43414525104fe1c6857f84e59447804917be
--- /dev/null
+++ b/data/lasagna/validity/noise_0.5/tasks_regression/per_epv.csv
@@ -0,0 +1,129 @@
+method,epv,validity
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+(R)ReliefF,8.5417,0.9322
+(R)ReliefF,19.3846,0.9346
+(R)ReliefF,43.0833,0.6944
+(R)ReliefF,541.4167,0.9433
+(R)ReliefF,891.0,0.8744
+(R)ReliefF,918.4,0.934
+(R)ReliefF,1448.5923,0.9704
+CART,5.6064,0.7819
+CART,8.5417,0.8633
+CART,19.3846,0.5824
+CART,43.0833,0.4422
+CART,541.4167,0.93
+CART,891.0,0.7278
+CART,918.4,0.9207
+CART,1448.5923,0.8341
+ElasticNet,5.6064,0.9735
+ElasticNet,8.5417,0.855
+ElasticNet,19.3846,0.8713
+ElasticNet,43.0833,0.5422
+ElasticNet,541.4167,0.93
+ElasticNet,891.0,0.96
+ElasticNet,918.4,0.9579
+ElasticNet,1448.5923,1.0
+F-Test,5.6064,1.0
+F-Test,8.5417,0.8894
+F-Test,19.3846,0.9625
+F-Test,43.0833,0.8356
+F-Test,541.4167,0.9433
+F-Test,891.0,0.9433
+F-Test,918.4,0.9446
+F-Test,1448.5923,1.0
+GainRatio,5.6064,0.0684
+GainRatio,8.5417,0.9339
+GainRatio,19.3846,0.9271
+GainRatio,43.0833,0.6367
+GainRatio,541.4167,0.93
+GainRatio,891.0,0.96
+GainRatio,918.4,0.934
+GainRatio,1448.5923,1.0
+LOCO,5.6064,0.5976
+LOCO,8.5417,0.8078
+LOCO,19.3846,0.8553
+LOCO,43.0833,0.7556
+LOCO,259.3049,0.975
+LOCO,302.086,0.4526
+LOCO,541.4167,0.9267
+LOCO,891.0,0.8189
+LOCO,918.4,0.9074
+LOCO,1448.5923,0.903
+LaplacianScore,5.6064,1.0
+LaplacianScore,8.5417,0.96
+LaplacianScore,19.3846,0.9346
+LaplacianScore,43.0833,0.9433
+LaplacianScore,541.4167,0.8211
+LaplacianScore,891.0,0.7611
+LaplacianScore,918.4,0.8514
+LaplacianScore,1448.5923,0.7122
+Lasso,5.6064,0.9735
+Lasso,8.5417,0.855
+Lasso,19.3846,0.8713
+Lasso,43.0833,0.5422
+Lasso,541.4167,0.93
+Lasso,891.0,0.7833
+Lasso,918.4,0.9579
+Lasso,1448.5923,1.0
+MI,5.6064,0.9736
+MI,8.5417,0.9517
+MI,19.3846,0.9192
+MI,43.0833,0.8089
+MI,541.4167,0.9433
+MI,891.0,0.96
+MI,918.4,0.9579
+MI,1448.5923,0.9951
+MarkovBlanket,5.6064,0.6411
+MarkovBlanket,8.5417,0.6772
+MarkovBlanket,19.3846,0.9346
+MarkovBlanket,43.0833,0.6633
+MarkovBlanket,541.4167,0.5833
+MarkovBlanket,891.0,0.9133
+MarkovBlanket,918.4,0.9312
+MarkovBlanket,1448.5923,0.9975
+RFImportance,5.6064,0.831
+RFImportance,8.5417,0.8694
+RFImportance,19.3846,0.6809
+RFImportance,43.0833,0.4256
+RFImportance,541.4167,0.93
+RFImportance,891.0,0.7833
+RFImportance,918.4,0.9102
+RFImportance,1448.5923,0.8198
+Random,5.6064,0.6912
+Random,8.5417,0.6272
+Random,19.3846,0.7088
+Random,43.0833,0.6633
+Random,259.3049,0.5667
+Random,302.086,0.7744
+Random,541.4167,0.5833
+Random,891.0,0.6556
+Random,918.4,0.7471
+Random,1448.5923,0.6982
+SBE,5.6064,0.6811
+SBE,8.5417,0.815
+SBE,19.3846,0.7081
+SBE,43.0833,0.68
+SBE,259.3049,0.6342
+SBE,302.086,0.7752
+SBE,541.4167,0.9433
+SBE,891.0,0.8744
+SBE,918.4,0.9207
+SBE,1448.5923,0.7125
+SFS,5.6064,0.735
+SFS,8.5417,0.7761
+SFS,19.3846,0.8207
+SFS,43.0833,0.9211
+SFS,259.3049,0.8942
+SFS,302.086,0.7201
+SFS,541.4167,0.8211
+SFS,891.0,0.8911
+SFS,918.4,0.9207
+SFS,1448.5923,0.8037
+mRMR,5.6064,0.1784
+mRMR,8.5417,0.7611
+mRMR,19.3846,0.6809
+mRMR,43.0833,0.5978
+mRMR,541.4167,0.5644
+mRMR,891.0,0.96
+mRMR,918.4,0.804
+mRMR,1448.5923,0.9413
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new file mode 100644
index 0000000000000000000000000000000000000000..a01c031f629cce01ff6109a7f0d89091d09041d0
--- /dev/null
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diff --git a/data/lasagna/validity/noise_0.75/tasks_classification/plain.svg b/data/lasagna/validity/noise_0.75/tasks_classification/plain.svg
new file mode 100644
index 0000000000000000000000000000000000000000..d8175c7dc1f1f2628c445a81ce2511005ace9c61
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new file mode 100644
index 0000000000000000000000000000000000000000..2fde664672b7cdb48ab3bfaaab5a4c3796a26541
--- /dev/null
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new file mode 100644
index 0000000000000000000000000000000000000000..518e34e7a66cd3d7b542742a32b91c5b2ff1cc2b
--- /dev/null
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diff --git a/data/lasagna/validity/noise_1/tasks_binary/per_epv.csv b/data/lasagna/validity/noise_1/tasks_binary/per_epv.csv
new file mode 100644
index 0000000000000000000000000000000000000000..fa68ae6bc81440a05551793cfe7d2cbe0b91c458
--- /dev/null
+++ b/data/lasagna/validity/noise_1/tasks_binary/per_epv.csv
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diff --git a/data/lasagna/validity/noise_1/tasks_multiclass/per_epv.csv b/data/lasagna/validity/noise_1/tasks_multiclass/per_epv.csv
new file mode 100644
index 0000000000000000000000000000000000000000..31d5dd67ea6eee119e20e47ce90702a7646cc3f4
--- /dev/null
+++ b/data/lasagna/validity/noise_1/tasks_multiclass/per_epv.csv
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+method,epv,validity
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+(R)ReliefF,0.0048,0.9938
+(R)ReliefF,0.1081,0.8874
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+(R)ReliefF,0.5882,0.8328
+(R)ReliefF,2.5,0.6572
+(R)ReliefF,20.7419,0.8946
+(R)ReliefF,22.0556,0.7814
+(R)ReliefF,1723.7273,0.8625
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+CART,0.0048,0.6093
+CART,0.1081,0.4218
+CART,0.1143,0.7215
+CART,0.2105,0.6523
+CART,0.2212,0.7893
+CART,0.2647,0.7248
+CART,0.5882,0.6292
+CART,2.5,0.5323
+CART,20.7419,0.8965
+CART,22.0556,0.5189
+CART,1723.7273,0.7889
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+ElasticNet,20.7419,1.0
+ElasticNet,22.0556,0.7747
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+GainRatio,0.2647,0.6767
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+GainRatio,20.7419,1.0
+GainRatio,22.0556,0.8036
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+LOCO,0.1081,0.6823
+LOCO,0.1143,0.5864
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+LOCO,0.2212,0.7551
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+LOCO,0.5882,0.6744
+LOCO,2.5,0.5186
+LOCO,20.7419,0.842
+LOCO,22.0556,0.6525
+LOCO,1723.7273,0.7591
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+LaplacianScore,0.0048,1.0
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+LaplacianScore,0.1143,0.8447
+LaplacianScore,0.2105,0.6304
+LaplacianScore,0.2212,0.2966
+LaplacianScore,0.2647,0.8837
+LaplacianScore,0.5882,0.7995
+LaplacianScore,2.5,0.7785
+LaplacianScore,20.7419,1.0
+LaplacianScore,22.0556,0.7672
+LaplacianScore,1723.7273,0.8204
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+Lasso,0.0048,0.8747
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+Lasso,0.2212,0.797
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+Lasso,0.5882,0.7486
+Lasso,2.5,0.6427
+Lasso,20.7419,1.0
+Lasso,22.0556,0.7511
+Lasso,1723.7273,0.8625
+MI,0.0013,0.9998
+MI,0.0048,0.9749
+MI,0.1081,0.8819
+MI,0.1143,0.8402
+MI,0.2105,0.7626
+MI,0.2212,0.9322
+MI,0.2647,0.8481
+MI,0.5882,0.8142
+MI,2.5,0.738
+MI,20.7419,1.0
+MI,22.0556,0.805
+MI,1723.7273,0.8443
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+MarkovBlanket,0.0048,0.421
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+MarkovBlanket,0.1143,0.5998
+MarkovBlanket,0.2105,0.4679
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+MarkovBlanket,0.2647,0.7019
+MarkovBlanket,0.5882,0.7664
+MarkovBlanket,2.5,0.3804
+MarkovBlanket,20.7419,0.041
+MarkovBlanket,22.0556,0.2947
+MarkovBlanket,1723.7273,0.7469
+RFImportance,0.0013,0.8608
+RFImportance,0.0048,0.9023
+RFImportance,0.1081,0.8381
+RFImportance,0.1143,0.823
+RFImportance,0.2105,0.6675
+RFImportance,0.2212,0.8465
+RFImportance,0.2647,0.7378
+RFImportance,0.5882,0.6754
+RFImportance,2.5,0.6465
+RFImportance,20.7419,0.9256
+RFImportance,22.0556,0.5686
+RFImportance,1723.7273,0.8204
+Random,0.0013,0.4506
+Random,0.0048,0.4648
+Random,0.1081,0.5549
+Random,0.1143,0.5139
+Random,0.2105,0.4956
+Random,0.2212,0.5748
+Random,0.2647,0.4563
+Random,0.5882,0.3503
+Random,2.5,0.4106
+Random,20.7419,0.5411
+Random,22.0556,0.5336
+Random,1723.7273,0.467
+SBE,0.0013,0.4949
+SBE,0.0048,0.5073
+SBE,0.1081,0.5615
+SBE,0.1143,0.5242
+SBE,0.2105,0.4804
+SBE,0.2212,0.4719
+SBE,0.2647,0.4844
+SBE,0.5882,0.6664
+SBE,2.5,0.5159
+SBE,20.7419,0.5071
+SBE,22.0556,0.5267
+SBE,1723.7273,0.7457
+SFS,0.0013,0.5248
+SFS,0.0048,0.4984
+SFS,0.1081,0.6543
+SFS,0.1143,0.6833
+SFS,0.2105,0.6939
+SFS,0.2212,0.4121
+SFS,0.2647,0.6959
+SFS,0.5882,0.6566
+SFS,2.5,0.702
+SFS,20.7419,0.5126
+SFS,22.0556,0.5586
+SFS,1723.7273,0.8022
+mRMR,0.0013,0.8346
+mRMR,0.0048,0.9638
+mRMR,0.1081,0.7199
+mRMR,0.1143,0.8402
+mRMR,0.2105,0.7379
+mRMR,0.2212,0.9424
+mRMR,0.2647,0.8041
+mRMR,0.5882,0.8142
+mRMR,2.5,0.7481
+mRMR,20.7419,1.0
+mRMR,22.0556,0.6411
+mRMR,1723.7273,0.4551
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new file mode 100644
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diff --git a/data/lasagna/validity/noise_1/tasks_regression/per_epv.csv b/data/lasagna/validity/noise_1/tasks_regression/per_epv.csv
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index 0000000000000000000000000000000000000000..d9e622e42f9cca55d8810deee0959a447e4b2ca3
--- /dev/null
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index eddf73dfd936946f5038da1d37aad1348e66d03b..954d52e86eb9208d37d9f0d22be545fe9f9492eb 100644
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       "tasks": "regression",
@@ -1501,7 +1720,8 @@
       "path": "subsets/tasks_regression/models_linearmodel/budgets_2",
       "n_datasets": 10,
       "n_methods": 15,
-      "n_comparisons": 450
+      "n_comparisons": 450,
+      "pareto": true
     },
     {
       "tasks": "regression",
@@ -1510,7 +1730,8 @@
       "path": "subsets/tasks_regression/models_linearmodel/budgets_3",
       "n_datasets": 10,
       "n_methods": 15,
-      "n_comparisons": 450
+      "n_comparisons": 450,
+      "pareto": true
     },
     {
       "tasks": "regression",
@@ -1519,7 +1740,8 @@
       "path": "subsets/tasks_regression/models_linearmodel/budgets_4",
       "n_datasets": 10,
       "n_methods": 15,
-      "n_comparisons": 450
+      "n_comparisons": 450,
+      "pareto": true
     },
     {
       "tasks": "regression",
@@ -1528,7 +1750,8 @@
       "path": "subsets/tasks_regression/models_randomforest/budgets_all",
       "n_datasets": 10,
       "n_methods": 15,
-      "n_comparisons": 2250
+      "n_comparisons": 2250,
+      "pareto": true
     },
     {
       "tasks": "regression",
@@ -1537,7 +1760,8 @@
       "path": "subsets/tasks_regression/models_randomforest/budgets_0",
       "n_datasets": 10,
       "n_methods": 15,
-      "n_comparisons": 450
+      "n_comparisons": 450,
+      "pareto": true
     },
     {
       "tasks": "regression",
@@ -1546,7 +1770,8 @@
       "path": "subsets/tasks_regression/models_randomforest/budgets_1",
       "n_datasets": 10,
       "n_methods": 15,
-      "n_comparisons": 450
+      "n_comparisons": 450,
+      "pareto": true
     },
     {
       "tasks": "regression",
@@ -1555,7 +1780,8 @@
       "path": "subsets/tasks_regression/models_randomforest/budgets_2",
       "n_datasets": 10,
       "n_methods": 15,
-      "n_comparisons": 450
+      "n_comparisons": 450,
+      "pareto": true
     },
     {
       "tasks": "regression",
@@ -1564,7 +1790,8 @@
       "path": "subsets/tasks_regression/models_randomforest/budgets_3",
       "n_datasets": 10,
       "n_methods": 15,
-      "n_comparisons": 450
+      "n_comparisons": 450,
+      "pareto": true
     },
     {
       "tasks": "regression",
@@ -1573,7 +1800,8 @@
       "path": "subsets/tasks_regression/models_randomforest/budgets_4",
       "n_datasets": 10,
       "n_methods": 15,
-      "n_comparisons": 450
+      "n_comparisons": 450,
+      "pareto": true
     },
     {
       "tasks": "regression",
@@ -1582,7 +1810,8 @@
       "path": "subsets/tasks_regression/models_ta-realmlp/budgets_all",
       "n_datasets": 10,
       "n_methods": 15,
-      "n_comparisons": 2250
+      "n_comparisons": 2250,
+      "pareto": true
     },
     {
       "tasks": "regression",
@@ -1591,7 +1820,8 @@
       "path": "subsets/tasks_regression/models_ta-realmlp/budgets_0",
       "n_datasets": 10,
       "n_methods": 15,
-      "n_comparisons": 450
+      "n_comparisons": 450,
+      "pareto": true
     },
     {
       "tasks": "regression",
@@ -1600,7 +1830,8 @@
       "path": "subsets/tasks_regression/models_ta-realmlp/budgets_1",
       "n_datasets": 10,
       "n_methods": 15,
-      "n_comparisons": 450
+      "n_comparisons": 450,
+      "pareto": true
     },
     {
       "tasks": "regression",
@@ -1609,7 +1840,8 @@
       "path": "subsets/tasks_regression/models_ta-realmlp/budgets_2",
       "n_datasets": 10,
       "n_methods": 15,
-      "n_comparisons": 450
+      "n_comparisons": 450,
+      "pareto": true
     },
     {
       "tasks": "regression",
@@ -1618,7 +1850,8 @@
       "path": "subsets/tasks_regression/models_ta-realmlp/budgets_3",
       "n_datasets": 10,
       "n_methods": 15,
-      "n_comparisons": 450
+      "n_comparisons": 450,
+      "pareto": true
     },
     {
       "tasks": "regression",
@@ -1627,7 +1860,8 @@
       "path": "subsets/tasks_regression/models_ta-realmlp/budgets_4",
       "n_datasets": 10,
       "n_methods": 15,
-      "n_comparisons": 450
+      "n_comparisons": 450,
+      "pareto": true
     },
     {
       "tasks": "regression",
@@ -1636,7 +1870,8 @@
       "path": "subsets/tasks_regression/models_ta-tabiclv2/budgets_all",
       "n_datasets": 10,
       "n_methods": 15,
-      "n_comparisons": 2250
+      "n_comparisons": 2250,
+      "pareto": true
     },
     {
       "tasks": "regression",
@@ -1645,7 +1880,8 @@
       "path": "subsets/tasks_regression/models_ta-tabiclv2/budgets_0",
       "n_datasets": 10,
       "n_methods": 15,
-      "n_comparisons": 450
+      "n_comparisons": 450,
+      "pareto": true
     },
     {
       "tasks": "regression",
@@ -1654,7 +1890,8 @@
       "path": "subsets/tasks_regression/models_ta-tabiclv2/budgets_1",
       "n_datasets": 10,
       "n_methods": 15,
-      "n_comparisons": 450
+      "n_comparisons": 450,
+      "pareto": true
     },
     {
       "tasks": "regression",
@@ -1663,7 +1900,8 @@
       "path": "subsets/tasks_regression/models_ta-tabiclv2/budgets_2",
       "n_datasets": 10,
       "n_methods": 15,
-      "n_comparisons": 450
+      "n_comparisons": 450,
+      "pareto": true
     },
     {
       "tasks": "regression",
@@ -1672,7 +1910,8 @@
       "path": "subsets/tasks_regression/models_ta-tabiclv2/budgets_3",
       "n_datasets": 10,
       "n_methods": 15,
-      "n_comparisons": 450
+      "n_comparisons": 450,
+      "pareto": true
     },
     {
       "tasks": "regression",
@@ -1681,7 +1920,8 @@
       "path": "subsets/tasks_regression/models_ta-tabiclv2/budgets_4",
       "n_datasets": 10,
       "n_methods": 15,
-      "n_comparisons": 450
+      "n_comparisons": 450,
+      "pareto": true
     }
   ]
 }
diff --git a/data/results.csv b/data/results.csv
new file mode 100644
index 0000000000000000000000000000000000000000..77475c9dbe4691dc4a66a34a5717c92f11c2ec01
--- /dev/null
+++ b/data/results.csv
@@ -0,0 +1,26626 @@
+dataset,task_type,method,model,budget,max_features,metric,error,error_std,n_splits,fs_time_s,fallback_pct
+allstate_claims_severity,regression,SFS,LightGBM,1,3.0,rmse,0.661267,0.000947,3,2990.135,0.0
+allstate_claims_severity,regression,CART,LightGBM,1,3.0,rmse,0.667041,0.001286,3,4.14,0.0
+allstate_claims_severity,regression,RFImportance,LightGBM,1,3.0,rmse,0.667041,0.001286,3,36.966,0.0
+allstate_claims_severity,regression,LOCO,LightGBM,1,3.0,rmse,0.66932,0.001282,3,2606.113,0.0
+allstate_claims_severity,regression,MI,LightGBM,1,3.0,rmse,0.669908,0.001717,3,55.926,0.0
+allstate_claims_severity,regression,F-Test,LightGBM,1,3.0,rmse,0.675362,0.001979,3,0.252,0.0
+allstate_claims_severity,regression,ElasticNet,LightGBM,1,3.0,rmse,0.680012,0.001797,3,5.976,0.0
+allstate_claims_severity,regression,Lasso,LightGBM,1,3.0,rmse,0.680012,0.001797,3,3.854,0.0
+allstate_claims_severity,regression,MarkovBlanket,LightGBM,1,3.0,rmse,0.693448,0.000123,3,10.508,0.0
+allstate_claims_severity,regression,(R)ReliefF,LightGBM,1,3.0,rmse,0.694837,0.002513,3,3600.471,0.0
+allstate_claims_severity,regression,GainRatio,LightGBM,1,3.0,rmse,0.704637,0.009895,3,35.951,0.0
+allstate_claims_severity,regression,mRMR,LightGBM,1,3.0,rmse,0.715821,0.012759,3,37.663,0.0
+allstate_claims_severity,regression,Random,LightGBM,1,3.0,rmse,0.782518,0.024443,3,0.0,100.0
+allstate_claims_severity,regression,SBE,LightGBM,1,3.0,rmse,0.784968,0.022667,3,3611.467,100.0
+allstate_claims_severity,regression,LaplacianScore,LightGBM,1,3.0,rmse,0.81113,0.00231,3,0.518,0.0
+allstate_claims_severity,regression,LOCO,LightGBM,2,6.0,rmse,0.633096,0.006086,3,2625.673,0.0
+allstate_claims_severity,regression,MarkovBlanket,LightGBM,2,6.0,rmse,0.636696,0.000852,3,10.338,0.0
+allstate_claims_severity,regression,CART,LightGBM,2,6.0,rmse,0.636724,0.003119,3,4.02,0.0
+allstate_claims_severity,regression,RFImportance,LightGBM,2,6.0,rmse,0.639174,0.0041,3,36.974,0.0
+allstate_claims_severity,regression,SFS,LightGBM,2,6.0,rmse,0.644213,0.004959,3,3609.663,100.0
+allstate_claims_severity,regression,MI,LightGBM,2,6.0,rmse,0.645509,0.001036,3,56.271,0.0
+allstate_claims_severity,regression,GainRatio,LightGBM,2,6.0,rmse,0.648317,0.002783,3,36.356,0.0
+allstate_claims_severity,regression,(R)ReliefF,LightGBM,2,6.0,rmse,0.650327,0.012035,3,3600.348,0.0
+allstate_claims_severity,regression,ElasticNet,LightGBM,2,6.0,rmse,0.65107,0.002146,3,5.868,0.0
+allstate_claims_severity,regression,Lasso,LightGBM,2,6.0,rmse,0.65107,0.002146,3,3.288,0.0
+allstate_claims_severity,regression,F-Test,LightGBM,2,6.0,rmse,0.658068,0.001475,3,0.255,0.0
+allstate_claims_severity,regression,mRMR,LightGBM,2,6.0,rmse,0.660437,0.013604,3,38.572,0.0
+allstate_claims_severity,regression,Random,LightGBM,2,6.0,rmse,0.784439,0.016154,3,0.0,100.0
+allstate_claims_severity,regression,SBE,LightGBM,2,6.0,rmse,0.792717,0.021032,3,3605.601,100.0
+allstate_claims_severity,regression,LaplacianScore,LightGBM,2,6.0,rmse,0.808393,0.002603,3,0.599,0.0
+allstate_claims_severity,regression,RFImportance,LightGBM,3,12.0,rmse,0.586983,0.001458,3,35.521,0.0
+allstate_claims_severity,regression,CART,LightGBM,3,12.0,rmse,0.587147,0.001137,3,4.027,0.0
+allstate_claims_severity,regression,GainRatio,LightGBM,3,12.0,rmse,0.599772,0.011167,3,36.598,0.0
+allstate_claims_severity,regression,ElasticNet,LightGBM,3,12.0,rmse,0.603106,0.004152,3,5.346,0.0
+allstate_claims_severity,regression,Lasso,LightGBM,3,12.0,rmse,0.603106,0.004152,3,2.722,0.0
+allstate_claims_severity,regression,LOCO,LightGBM,3,12.0,rmse,0.604076,0.00728,3,2765.1,0.0
+allstate_claims_severity,regression,MarkovBlanket,LightGBM,3,12.0,rmse,0.613204,0.001508,3,10.476,0.0
+allstate_claims_severity,regression,mRMR,LightGBM,3,12.0,rmse,0.614798,0.00062,3,40.582,0.0
+allstate_claims_severity,regression,MI,LightGBM,3,12.0,rmse,0.615689,0.001562,3,56.71,0.0
+allstate_claims_severity,regression,SFS,LightGBM,3,12.0,rmse,0.631286,0.018005,3,3607.412,100.0
+allstate_claims_severity,regression,(R)ReliefF,LightGBM,3,12.0,rmse,0.635676,0.012774,3,3600.256,0.0
+allstate_claims_severity,regression,F-Test,LightGBM,3,12.0,rmse,0.642307,0.001129,3,0.252,0.0
+allstate_claims_severity,regression,Random,LightGBM,3,12.0,rmse,0.729117,0.021726,3,0.0,100.0
+allstate_claims_severity,regression,SBE,LightGBM,3,12.0,rmse,0.733782,0.011959,3,3616.553,100.0
+allstate_claims_severity,regression,LaplacianScore,LightGBM,3,12.0,rmse,0.807038,0.002026,3,0.451,0.0
+allstate_claims_severity,regression,RFImportance,LightGBM,4,26.0,rmse,0.557917,0.000505,3,35.307,0.0
+allstate_claims_severity,regression,CART,LightGBM,4,26.0,rmse,0.558341,0.002423,3,3.955,0.0
+allstate_claims_severity,regression,ElasticNet,LightGBM,4,26.0,rmse,0.561783,0.004046,3,5.234,0.0
+allstate_claims_severity,regression,Lasso,LightGBM,4,26.0,rmse,0.561783,0.004046,3,3.003,0.0
+allstate_claims_severity,regression,LOCO,LightGBM,4,26.0,rmse,0.564008,0.005724,3,2819.973,0.0
+allstate_claims_severity,regression,MI,LightGBM,4,26.0,rmse,0.565449,0.00243,3,56.204,0.0
+allstate_claims_severity,regression,GainRatio,LightGBM,4,26.0,rmse,0.568615,0.001043,3,36.678,0.0
+allstate_claims_severity,regression,(R)ReliefF,LightGBM,4,26.0,rmse,0.592836,0.010503,3,3600.135,0.0
+allstate_claims_severity,regression,mRMR,LightGBM,4,26.0,rmse,0.601614,0.002312,3,44.896,0.0
+allstate_claims_severity,regression,SFS,LightGBM,4,26.0,rmse,0.604767,0.021621,3,3612.511,100.0
+allstate_claims_severity,regression,F-Test,LightGBM,4,26.0,rmse,0.611501,0.007628,3,0.257,0.0
+allstate_claims_severity,regression,MarkovBlanket,LightGBM,4,26.0,rmse,0.613336,0.000752,3,10.471,0.0
+allstate_claims_severity,regression,Random,LightGBM,4,26.0,rmse,0.669133,0.033268,3,0.0,100.0
+allstate_claims_severity,regression,SBE,LightGBM,4,26.0,rmse,0.671587,0.02986,3,3609.86,100.0
+allstate_claims_severity,regression,LaplacianScore,LightGBM,4,26.0,rmse,0.777816,0.009451,3,0.482,0.0
+allstate_claims_severity,regression,ElasticNet,LightGBM,5,58.0,rmse,0.541075,0.001106,3,5.227,0.0
+allstate_claims_severity,regression,Lasso,LightGBM,5,58.0,rmse,0.541075,0.001106,3,2.818,0.0
+allstate_claims_severity,regression,LOCO,LightGBM,5,58.0,rmse,0.543675,0.003088,3,2507.71,0.0
+allstate_claims_severity,regression,CART,LightGBM,5,58.0,rmse,0.544834,0.000727,3,4.068,0.0
+allstate_claims_severity,regression,RFImportance,LightGBM,5,58.0,rmse,0.545078,0.000824,3,36.074,0.0
+allstate_claims_severity,regression,MI,LightGBM,5,58.0,rmse,0.547029,0.000853,3,55.81,0.0
+allstate_claims_severity,regression,MarkovBlanket,LightGBM,5,58.0,rmse,0.554707,0.000743,3,10.299,0.0
+allstate_claims_severity,regression,F-Test,LightGBM,5,58.0,rmse,0.554788,0.003866,3,0.263,0.0
+allstate_claims_severity,regression,GainRatio,LightGBM,5,58.0,rmse,0.562298,0.000754,3,36.119,0.0
+allstate_claims_severity,regression,(R)ReliefF,LightGBM,5,58.0,rmse,0.567665,0.008799,3,3600.357,0.0
+allstate_claims_severity,regression,SFS,LightGBM,5,58.0,rmse,0.577796,0.00488,3,3607.353,100.0
+allstate_claims_severity,regression,mRMR,LightGBM,5,58.0,rmse,0.581108,0.008409,3,51.117,0.0
+allstate_claims_severity,regression,Random,LightGBM,5,58.0,rmse,0.598239,0.016595,3,0.0,100.0
+allstate_claims_severity,regression,SBE,LightGBM,5,58.0,rmse,0.608456,0.018253,3,3607.785,100.0
+allstate_claims_severity,regression,LaplacianScore,LightGBM,5,58.0,rmse,0.681,0.011394,3,0.83,0.0
+allstate_claims_severity,regression,SFS,LinearModel,1,3.0,rmse,0.668386,0.004011,3,3041.71,0.0
+allstate_claims_severity,regression,LOCO,LinearModel,1,3.0,rmse,0.671732,0.001262,3,2611.114,0.0
+allstate_claims_severity,regression,MI,LinearModel,1,3.0,rmse,0.672055,0.001542,3,55.149,0.0
+allstate_claims_severity,regression,F-Test,LinearModel,1,3.0,rmse,0.676541,0.002022,3,0.257,0.0
+allstate_claims_severity,regression,CART,LinearModel,1,3.0,rmse,0.679458,0.001754,3,4.039,0.0
+allstate_claims_severity,regression,RFImportance,LinearModel,1,3.0,rmse,0.679458,0.001754,3,36.178,0.0
+allstate_claims_severity,regression,ElasticNet,LinearModel,1,3.0,rmse,0.680839,0.001762,3,5.397,0.0
+allstate_claims_severity,regression,Lasso,LinearModel,1,3.0,rmse,0.680839,0.001762,3,2.9,0.0
+allstate_claims_severity,regression,(R)ReliefF,LinearModel,1,3.0,rmse,0.695003,0.002524,3,3600.374,0.0
+allstate_claims_severity,regression,MarkovBlanket,LinearModel,1,3.0,rmse,0.702351,0.002541,3,10.399,0.0
+allstate_claims_severity,regression,GainRatio,LinearModel,1,3.0,rmse,0.712042,0.005969,3,36.289,0.0
+allstate_claims_severity,regression,mRMR,LinearModel,1,3.0,rmse,0.721902,0.009995,3,37.442,0.0
+allstate_claims_severity,regression,Random,LinearModel,1,3.0,rmse,0.783694,0.023767,3,0.0,100.0
+allstate_claims_severity,regression,SBE,LinearModel,1,3.0,rmse,0.787107,0.023989,3,3614.1,100.0
+allstate_claims_severity,regression,LaplacianScore,LinearModel,1,3.0,rmse,0.811116,0.002307,3,0.558,0.0
+allstate_claims_severity,regression,LOCO,LinearModel,2,6.0,rmse,0.648073,0.010878,3,2675.746,0.0
+allstate_claims_severity,regression,MI,LinearModel,2,6.0,rmse,0.653549,0.001078,3,56.467,0.0
+allstate_claims_severity,regression,MarkovBlanket,LinearModel,2,6.0,rmse,0.653594,0.000773,3,10.254,0.0
+allstate_claims_severity,regression,ElasticNet,LinearModel,2,6.0,rmse,0.655314,0.001299,3,5.665,0.0
+allstate_claims_severity,regression,Lasso,LinearModel,2,6.0,rmse,0.655314,0.001299,3,2.984,0.0
+allstate_claims_severity,regression,CART,LinearModel,2,6.0,rmse,0.65546,0.00141,3,4.129,0.0
+allstate_claims_severity,regression,(R)ReliefF,LinearModel,2,6.0,rmse,0.655769,0.009874,3,3600.261,0.0
+allstate_claims_severity,regression,SFS,LinearModel,2,6.0,rmse,0.655923,0.008389,3,3607.197,100.0
+allstate_claims_severity,regression,RFImportance,LinearModel,2,6.0,rmse,0.657657,0.004217,3,37.136,0.0
+allstate_claims_severity,regression,F-Test,LinearModel,2,6.0,rmse,0.6617,0.001937,3,0.259,0.0
+allstate_claims_severity,regression,GainRatio,LinearModel,2,6.0,rmse,0.661767,0.005568,3,35.946,0.0
+allstate_claims_severity,regression,mRMR,LinearModel,2,6.0,rmse,0.684844,0.014459,3,38.279,0.0
+allstate_claims_severity,regression,Random,LinearModel,2,6.0,rmse,0.789249,0.011984,3,0.0,100.0
+allstate_claims_severity,regression,SBE,LinearModel,2,6.0,rmse,0.796842,0.018303,3,3613.219,100.0
+allstate_claims_severity,regression,LaplacianScore,LinearModel,2,6.0,rmse,0.808321,0.002692,3,0.61,0.0
+allstate_claims_severity,regression,CART,LinearModel,3,12.0,rmse,0.608482,0.001986,3,3.968,0.0
+allstate_claims_severity,regression,RFImportance,LinearModel,3,12.0,rmse,0.608517,0.001973,3,36.133,0.0
+allstate_claims_severity,regression,ElasticNet,LinearModel,3,12.0,rmse,0.618777,0.004484,3,5.341,0.0
+allstate_claims_severity,regression,Lasso,LinearModel,3,12.0,rmse,0.618777,0.004484,3,2.868,0.0
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+forest_fires,regression,SBE,TA-RealMLP,3,6.0,rmse,1.402574,0.027574,3,126.072,0.0
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+forest_fires,regression,SBE,TA-RealMLP,4,8.0,rmse,1.41388,0.032597,3,91.579,0.0
+forest_fires,regression,LaplacianScore,TA-RealMLP,4,8.0,rmse,1.417805,0.064263,3,0.022,0.0
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+forest_fires,regression,MI,TA-RealMLP,4,8.0,rmse,1.419287,0.071412,3,0.176,0.0
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+superconductivity,regression,Random,LightGBM,3,41.0,rmse,9.446108,0.034382,3,0.0,100.0
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+superconductivity,regression,GainRatio,LinearModel,1,14.0,rmse,20.689164,0.082039,3,2.534,0.0
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+superconductivity,regression,Lasso,LinearModel,1,14.0,rmse,20.982724,0.278556,3,1.695,0.0
+superconductivity,regression,RFImportance,LinearModel,1,14.0,rmse,21.050124,0.470381,3,8.236,0.0
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+superconductivity,regression,LaplacianScore,LinearModel,1,14.0,rmse,22.051675,0.167531,3,0.241,0.0
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+superconductivity,regression,MI,LinearModel,2,27.0,rmse,19.220838,0.178102,3,3.191,0.0
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+superconductivity,regression,GainRatio,LinearModel,2,27.0,rmse,19.64436,0.222363,3,2.517,0.0
+superconductivity,regression,LaplacianScore,LinearModel,2,27.0,rmse,19.720962,0.149251,3,0.28,0.0
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+superconductivity,regression,LOCO,LinearModel,2,27.0,rmse,19.911118,0.607917,3,1252.851,0.0
+superconductivity,regression,mRMR,LinearModel,2,27.0,rmse,20.076876,0.273268,3,4.18,0.0
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+superconductivity,regression,Random,LinearModel,2,27.0,rmse,20.27849,0.178194,3,0.0,100.0
+superconductivity,regression,(R)ReliefF,LinearModel,2,27.0,rmse,20.380115,0.162817,3,757.176,0.0
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+superconductivity,regression,ElasticNet,LinearModel,3,41.0,rmse,18.232782,0.216509,3,1.985,0.0
+superconductivity,regression,Lasso,LinearModel,3,41.0,rmse,18.251917,0.129888,3,1.456,0.0
+superconductivity,regression,CART,LinearModel,3,41.0,rmse,18.290196,0.036281,3,0.884,0.0
+superconductivity,regression,mRMR,LinearModel,3,41.0,rmse,18.591581,0.2513,3,4.679,0.0
+superconductivity,regression,F-Test,LinearModel,3,41.0,rmse,18.727551,0.116549,3,0.023,0.0
+superconductivity,regression,SFS,LinearModel,3,41.0,rmse,18.824914,0.575146,3,3601.979,100.0
+superconductivity,regression,MI,LinearModel,3,41.0,rmse,18.888708,0.17509,3,3.192,0.0
+superconductivity,regression,LOCO,LinearModel,3,41.0,rmse,18.968276,0.339728,3,1224.0,0.0
+superconductivity,regression,LaplacianScore,LinearModel,3,41.0,rmse,18.994597,0.080285,3,0.172,0.0
+superconductivity,regression,GainRatio,LinearModel,3,41.0,rmse,19.02649,0.083314,3,2.491,0.0
+superconductivity,regression,(R)ReliefF,LinearModel,3,41.0,rmse,19.046561,0.230001,3,742.348,0.0
+superconductivity,regression,SBE,LinearModel,3,41.0,rmse,19.118028,0.337923,3,3603.593,100.0
+superconductivity,regression,MarkovBlanket,LinearModel,3,41.0,rmse,19.205311,0.551552,3,0.562,0.0
+superconductivity,regression,Random,LinearModel,3,41.0,rmse,19.288456,0.56109,3,0.0,100.0
+superconductivity,regression,Lasso,LinearModel,4,54.0,rmse,17.784367,0.156509,3,2.074,0.0
+superconductivity,regression,RFImportance,LinearModel,4,54.0,rmse,17.849234,0.163585,3,8.485,0.0
+superconductivity,regression,ElasticNet,LinearModel,4,54.0,rmse,17.923537,0.121166,3,1.988,0.0
+superconductivity,regression,CART,LinearModel,4,54.0,rmse,18.000851,0.121275,3,0.869,0.0
+superconductivity,regression,mRMR,LinearModel,4,54.0,rmse,18.005302,0.117681,3,5.087,0.0
+superconductivity,regression,LaplacianScore,LinearModel,4,54.0,rmse,18.200688,0.135851,3,0.21,0.0
+superconductivity,regression,GainRatio,LinearModel,4,54.0,rmse,18.249985,0.159846,3,2.504,0.0
+superconductivity,regression,(R)ReliefF,LinearModel,4,54.0,rmse,18.264604,0.286976,3,804.833,0.0
+superconductivity,regression,LOCO,LinearModel,4,54.0,rmse,18.316977,0.14775,3,1256.699,0.0
+superconductivity,regression,Random,LinearModel,4,54.0,rmse,18.345059,0.391582,3,0.0,100.0
+superconductivity,regression,F-Test,LinearModel,4,54.0,rmse,18.352255,0.09315,3,0.03,0.0
+superconductivity,regression,SFS,LinearModel,4,54.0,rmse,18.39368,0.167148,3,3604.56,100.0
+superconductivity,regression,SBE,LinearModel,4,54.0,rmse,18.408552,0.205281,3,3607.546,100.0
+superconductivity,regression,MarkovBlanket,LinearModel,4,54.0,rmse,18.496165,0.24899,3,0.471,0.0
+superconductivity,regression,MI,LinearModel,4,54.0,rmse,18.557831,0.135763,3,3.153,0.0
+superconductivity,regression,(R)ReliefF,LinearModel,5,68.0,rmse,17.62743,0.114014,3,793.193,0.0
+superconductivity,regression,Random,LinearModel,5,68.0,rmse,17.641248,0.064006,3,0.0,100.0
+superconductivity,regression,Lasso,LinearModel,5,68.0,rmse,17.645687,0.184305,3,1.767,0.0
+superconductivity,regression,RFImportance,LinearModel,5,68.0,rmse,17.657135,0.063798,3,9.306,0.0
+superconductivity,regression,mRMR,LinearModel,5,68.0,rmse,17.670697,0.129691,3,5.546,0.0
+superconductivity,regression,SBE,LinearModel,5,68.0,rmse,17.735597,0.091373,3,3605.065,100.0
+superconductivity,regression,ElasticNet,LinearModel,5,68.0,rmse,17.743198,0.098196,3,1.951,0.0
+superconductivity,regression,SFS,LinearModel,5,68.0,rmse,17.748904,0.184921,3,3606.133,100.0
+superconductivity,regression,CART,LinearModel,5,68.0,rmse,17.779132,0.072975,3,0.878,0.0
+superconductivity,regression,MI,LinearModel,5,68.0,rmse,17.779756,0.102266,3,3.162,0.0
+superconductivity,regression,LOCO,LinearModel,5,68.0,rmse,17.814452,0.067485,3,1128.965,0.0
+superconductivity,regression,GainRatio,LinearModel,5,68.0,rmse,17.81685,0.093422,3,2.485,0.0
+superconductivity,regression,LaplacianScore,LinearModel,5,68.0,rmse,17.820269,0.107443,3,0.304,0.0
+superconductivity,regression,F-Test,LinearModel,5,68.0,rmse,17.882215,0.10612,3,0.026,0.0
+superconductivity,regression,MarkovBlanket,LinearModel,5,68.0,rmse,17.984632,0.245465,3,0.449,0.0
+superconductivity,regression,ElasticNet,RandomForest,1,14.0,rmse,9.858727,0.011245,3,2.329,0.0
+superconductivity,regression,RFImportance,RandomForest,1,14.0,rmse,9.899981,0.177763,3,8.19,0.0
+superconductivity,regression,mRMR,RandomForest,1,14.0,rmse,9.905365,0.019967,3,3.227,0.0
+superconductivity,regression,GainRatio,RandomForest,1,14.0,rmse,9.929103,0.102917,3,2.599,0.0
+superconductivity,regression,SFS,RandomForest,1,14.0,rmse,9.930897,0.2048,3,3609.398,100.0
+superconductivity,regression,CART,RandomForest,1,14.0,rmse,9.966344,0.253247,3,0.916,0.0
+superconductivity,regression,Random,RandomForest,1,14.0,rmse,9.969228,0.077525,3,0.0,100.0
+superconductivity,regression,MarkovBlanket,RandomForest,1,14.0,rmse,9.980352,0.099566,3,0.494,0.0
+superconductivity,regression,SBE,RandomForest,1,14.0,rmse,9.995905,0.051275,3,3604.929,100.0
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+superconductivity,regression,LOCO,RandomForest,1,14.0,rmse,10.061128,0.076711,3,1317.19,0.0
+superconductivity,regression,(R)ReliefF,RandomForest,1,14.0,rmse,10.117949,0.172871,3,790.255,0.0
+superconductivity,regression,F-Test,RandomForest,1,14.0,rmse,10.368591,0.065568,3,0.024,0.0
+superconductivity,regression,LaplacianScore,RandomForest,1,14.0,rmse,10.393251,0.235985,3,0.225,0.0
+superconductivity,regression,MI,RandomForest,1,14.0,rmse,10.949943,0.639427,3,3.087,0.0
+superconductivity,regression,MI,RandomForest,2,27.0,rmse,9.63565,0.020861,3,3.107,0.0
+superconductivity,regression,RFImportance,RandomForest,2,27.0,rmse,9.666751,0.087252,3,8.543,0.0
+superconductivity,regression,ElasticNet,RandomForest,2,27.0,rmse,9.690409,0.068402,3,2.254,0.0
+superconductivity,regression,LaplacianScore,RandomForest,2,27.0,rmse,9.716699,0.096847,3,0.288,0.0
+superconductivity,regression,SFS,RandomForest,2,27.0,rmse,9.720988,0.024193,3,3603.761,100.0
+superconductivity,regression,mRMR,RandomForest,2,27.0,rmse,9.723795,0.159093,3,4.02,0.0
+superconductivity,regression,CART,RandomForest,2,27.0,rmse,9.730429,0.1322,3,0.916,0.0
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+superconductivity,regression,(R)ReliefF,RandomForest,2,27.0,rmse,9.756522,0.132257,3,781.709,0.0
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+superconductivity,regression,Lasso,RandomForest,2,27.0,rmse,9.791349,0.123135,3,2.142,0.0
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+superconductivity,regression,Random,RandomForest,2,27.0,rmse,9.831387,0.143488,3,0.0,100.0
+superconductivity,regression,mRMR,RandomForest,3,41.0,rmse,9.580041,0.100138,3,4.71,0.0
+superconductivity,regression,LOCO,RandomForest,3,41.0,rmse,9.596038,0.067028,3,1237.368,0.0
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+superconductivity,regression,GainRatio,RandomForest,3,41.0,rmse,9.642199,0.083533,3,2.53,0.0
+superconductivity,regression,MI,RandomForest,3,41.0,rmse,9.645921,0.02525,3,3.201,0.0
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+superconductivity,regression,MarkovBlanket,RandomForest,3,41.0,rmse,9.686046,0.053732,3,0.521,0.0
+superconductivity,regression,SFS,RandomForest,3,41.0,rmse,9.698647,0.003752,3,3604.911,100.0
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+superconductivity,regression,SBE,RandomForest,3,41.0,rmse,9.722776,0.106972,3,3604.842,100.0
+superconductivity,regression,Random,RandomForest,3,41.0,rmse,9.724234,0.121653,3,0.0,100.0
+superconductivity,regression,(R)ReliefF,RandomForest,4,54.0,rmse,9.528434,0.084512,3,788.941,0.0
+superconductivity,regression,MI,RandomForest,4,54.0,rmse,9.539782,0.058617,3,3.218,0.0
+superconductivity,regression,LOCO,RandomForest,4,54.0,rmse,9.56762,0.114112,3,1164.039,0.0
+superconductivity,regression,GainRatio,RandomForest,4,54.0,rmse,9.595489,0.055726,3,2.582,0.0
+superconductivity,regression,SFS,RandomForest,4,54.0,rmse,9.596146,0.068258,3,3604.057,100.0
+superconductivity,regression,MarkovBlanket,RandomForest,4,54.0,rmse,9.599846,0.077154,3,0.491,0.0
+superconductivity,regression,mRMR,RandomForest,4,54.0,rmse,9.607044,0.072573,3,5.043,0.0
+superconductivity,regression,RFImportance,RandomForest,4,54.0,rmse,9.610302,0.084583,3,8.262,0.0
+superconductivity,regression,F-Test,RandomForest,4,54.0,rmse,9.618227,0.049615,3,0.02,0.0
+superconductivity,regression,CART,RandomForest,4,54.0,rmse,9.622016,0.053183,3,0.88,0.0
+superconductivity,regression,LaplacianScore,RandomForest,4,54.0,rmse,9.638945,0.058137,3,0.24,0.0
+superconductivity,regression,Lasso,RandomForest,4,54.0,rmse,9.665823,0.069549,3,1.685,0.0
+superconductivity,regression,ElasticNet,RandomForest,4,54.0,rmse,9.668682,0.047837,3,2.196,0.0
+superconductivity,regression,Random,RandomForest,4,54.0,rmse,9.673377,0.090129,3,0.0,100.0
+superconductivity,regression,SBE,RandomForest,4,54.0,rmse,9.735907,0.152469,3,3608.072,100.0
+superconductivity,regression,(R)ReliefF,RandomForest,5,68.0,rmse,9.490626,0.054714,3,788.687,0.0
+superconductivity,regression,SFS,RandomForest,5,68.0,rmse,9.523077,0.11115,3,3607.058,100.0
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+superconductivity,regression,MI,RandomForest,5,68.0,rmse,9.545412,0.069846,3,3.185,0.0
+superconductivity,regression,LaplacianScore,RandomForest,5,68.0,rmse,9.558275,0.064716,3,0.2,0.0
+superconductivity,regression,MarkovBlanket,RandomForest,5,68.0,rmse,9.559115,0.113243,3,0.474,0.0
+superconductivity,regression,RFImportance,RandomForest,5,68.0,rmse,9.575084,0.057695,3,8.758,0.0
+superconductivity,regression,Random,RandomForest,5,68.0,rmse,9.586259,0.05218,3,0.0,100.0
+superconductivity,regression,CART,RandomForest,5,68.0,rmse,9.589418,0.047257,3,0.873,0.0
+superconductivity,regression,SBE,RandomForest,5,68.0,rmse,9.591292,0.056514,3,3603.788,100.0
+superconductivity,regression,ElasticNet,RandomForest,5,68.0,rmse,9.596083,0.050144,3,1.988,0.0
+superconductivity,regression,mRMR,RandomForest,5,68.0,rmse,9.607325,0.037386,3,5.596,0.0
+superconductivity,regression,GainRatio,RandomForest,5,68.0,rmse,9.608024,0.023823,3,2.507,0.0
+superconductivity,regression,F-Test,RandomForest,5,68.0,rmse,9.611693,0.049755,3,0.027,0.0
+superconductivity,regression,Lasso,RandomForest,5,68.0,rmse,9.619904,0.069752,3,1.533,0.0
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+superconductivity,regression,CART,TA-RealMLP,1,14.0,rmse,9.847259,0.101219,3,1.003,0.0
+superconductivity,regression,ElasticNet,TA-RealMLP,1,14.0,rmse,9.857107,0.127959,3,2.444,0.0
+superconductivity,regression,Random,TA-RealMLP,1,14.0,rmse,9.858668,0.213129,3,0.0,100.0
+superconductivity,regression,MarkovBlanket,TA-RealMLP,1,14.0,rmse,9.861491,0.071158,3,0.835,0.0
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+superconductivity,regression,Lasso,TA-RealMLP,1,14.0,rmse,9.931473,0.154721,3,1.478,0.0
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+superconductivity,regression,SFS,TA-RealMLP,1,14.0,rmse,9.971652,0.084454,3,3603.072,100.0
+superconductivity,regression,F-Test,TA-RealMLP,1,14.0,rmse,10.420681,0.191027,3,0.032,0.0
+superconductivity,regression,GainRatio,TA-RealMLP,1,14.0,rmse,10.66489,0.099369,3,3.165,0.0
+superconductivity,regression,MI,TA-RealMLP,1,14.0,rmse,11.761063,0.134765,3,3.855,0.0
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+superconductivity,regression,mRMR,TA-RealMLP,2,27.0,rmse,9.467147,0.074357,3,5.78,0.0
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+superconductivity,regression,ElasticNet,TA-RealMLP,2,27.0,rmse,9.572298,0.102313,3,2.389,0.0
+superconductivity,regression,MarkovBlanket,TA-RealMLP,2,27.0,rmse,9.573549,0.124439,3,0.738,0.0
+superconductivity,regression,Lasso,TA-RealMLP,2,27.0,rmse,9.586394,0.089428,3,1.51,0.0
+superconductivity,regression,Random,TA-RealMLP,2,27.0,rmse,9.671416,0.183475,3,0.0,100.0
+superconductivity,regression,SFS,TA-RealMLP,2,27.0,rmse,9.696691,0.10866,3,3604.487,100.0
+superconductivity,regression,F-Test,TA-RealMLP,2,27.0,rmse,9.868593,0.131814,3,0.024,0.0
+superconductivity,regression,GainRatio,TA-RealMLP,2,27.0,rmse,9.926059,0.010041,3,3.247,0.0
+superconductivity,regression,LaplacianScore,TA-RealMLP,2,27.0,rmse,10.118463,0.078013,3,0.241,0.0
+superconductivity,regression,MI,TA-RealMLP,2,27.0,rmse,10.15327,0.201347,3,3.82,0.0
+superconductivity,regression,MarkovBlanket,TA-RealMLP,3,41.0,rmse,9.430555,0.15468,3,0.745,0.0
+superconductivity,regression,CART,TA-RealMLP,3,41.0,rmse,9.435189,0.080623,3,1.006,0.0
+superconductivity,regression,mRMR,TA-RealMLP,3,41.0,rmse,9.475456,0.063511,3,6.672,0.0
+superconductivity,regression,ElasticNet,TA-RealMLP,3,41.0,rmse,9.479457,0.135465,3,2.412,0.0
+superconductivity,regression,Lasso,TA-RealMLP,3,41.0,rmse,9.481748,0.102105,3,1.419,0.0
+superconductivity,regression,(R)ReliefF,TA-RealMLP,3,41.0,rmse,9.494701,0.04624,3,1024.206,0.0
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+superconductivity,regression,RFImportance,TA-RealMLP,3,41.0,rmse,9.501578,0.076136,3,11.019,0.0
+superconductivity,regression,Random,TA-RealMLP,3,41.0,rmse,9.530478,0.113663,3,0.0,100.0
+superconductivity,regression,SFS,TA-RealMLP,3,41.0,rmse,9.559508,0.13295,3,3601.747,100.0
+superconductivity,regression,SBE,TA-RealMLP,3,41.0,rmse,9.600628,0.191293,3,3613.174,100.0
+superconductivity,regression,GainRatio,TA-RealMLP,3,41.0,rmse,9.62671,0.06445,3,3.189,0.0
+superconductivity,regression,F-Test,TA-RealMLP,3,41.0,rmse,9.64926,0.061781,3,0.026,0.0
+superconductivity,regression,LaplacianScore,TA-RealMLP,3,41.0,rmse,9.808883,0.052514,3,0.209,0.0
+superconductivity,regression,MI,TA-RealMLP,3,41.0,rmse,9.974351,0.139345,3,3.834,0.0
+superconductivity,regression,MarkovBlanket,TA-RealMLP,4,54.0,rmse,9.390602,0.105693,3,0.703,0.0
+superconductivity,regression,CART,TA-RealMLP,4,54.0,rmse,9.394813,0.11035,3,1.007,0.0
+superconductivity,regression,Lasso,TA-RealMLP,4,54.0,rmse,9.408713,0.109099,3,1.181,0.0
+superconductivity,regression,LOCO,TA-RealMLP,4,54.0,rmse,9.418293,0.052278,3,1391.323,0.0
+superconductivity,regression,(R)ReliefF,TA-RealMLP,4,54.0,rmse,9.432339,0.078598,3,1030.518,0.0
+superconductivity,regression,mRMR,TA-RealMLP,4,54.0,rmse,9.436027,0.103239,3,7.457,0.0
+superconductivity,regression,ElasticNet,TA-RealMLP,4,54.0,rmse,9.458263,0.101935,3,2.385,0.0
+superconductivity,regression,RFImportance,TA-RealMLP,4,54.0,rmse,9.479105,0.096686,3,11.037,0.0
+superconductivity,regression,SFS,TA-RealMLP,4,54.0,rmse,9.487106,0.068577,3,3609.885,100.0
+superconductivity,regression,SBE,TA-RealMLP,4,54.0,rmse,9.488511,0.04124,3,3612.478,100.0
+superconductivity,regression,F-Test,TA-RealMLP,4,54.0,rmse,9.499198,0.112097,3,0.025,0.0
+superconductivity,regression,Random,TA-RealMLP,4,54.0,rmse,9.505411,0.071913,3,0.0,100.0
+superconductivity,regression,GainRatio,TA-RealMLP,4,54.0,rmse,9.520634,0.004945,3,3.249,0.0
+superconductivity,regression,MI,TA-RealMLP,4,54.0,rmse,9.567502,0.067091,3,3.819,0.0
+superconductivity,regression,LaplacianScore,TA-RealMLP,4,54.0,rmse,9.602344,0.034388,3,0.235,0.0
+superconductivity,regression,mRMR,TA-RealMLP,5,68.0,rmse,9.349469,0.070573,3,7.724,0.0
+superconductivity,regression,SBE,TA-RealMLP,5,68.0,rmse,9.422066,0.070616,3,3611.282,100.0
+superconductivity,regression,Lasso,TA-RealMLP,5,68.0,rmse,9.424119,0.107287,3,1.168,0.0
+superconductivity,regression,SFS,TA-RealMLP,5,68.0,rmse,9.426321,0.095196,3,3605.712,100.0
+superconductivity,regression,CART,TA-RealMLP,5,68.0,rmse,9.430454,0.130974,3,1.014,0.0
+superconductivity,regression,Random,TA-RealMLP,5,68.0,rmse,9.431707,0.075698,3,0.0,100.0
+superconductivity,regression,RFImportance,TA-RealMLP,5,68.0,rmse,9.43333,0.126648,3,10.695,0.0
+superconductivity,regression,(R)ReliefF,TA-RealMLP,5,68.0,rmse,9.437521,0.135125,3,1026.821,0.0
+superconductivity,regression,MarkovBlanket,TA-RealMLP,5,68.0,rmse,9.437529,0.086314,3,0.666,0.0
+superconductivity,regression,LOCO,TA-RealMLP,5,68.0,rmse,9.44706,0.076791,3,1359.786,0.0
+superconductivity,regression,MI,TA-RealMLP,5,68.0,rmse,9.463866,0.12596,3,3.803,0.0
+superconductivity,regression,LaplacianScore,TA-RealMLP,5,68.0,rmse,9.468387,0.069339,3,0.212,0.0
+superconductivity,regression,GainRatio,TA-RealMLP,5,68.0,rmse,9.483335,0.055668,3,3.182,0.0
+superconductivity,regression,ElasticNet,TA-RealMLP,5,68.0,rmse,9.496992,0.14113,3,2.287,0.0
+superconductivity,regression,F-Test,TA-RealMLP,5,68.0,rmse,9.502724,0.198658,3,0.025,0.0
+superconductivity,regression,ElasticNet,TA-TabICLv2,1,14.0,rmse,9.006864,0.072098,3,2.085,0.0
+superconductivity,regression,MarkovBlanket,TA-TabICLv2,1,14.0,rmse,9.024575,0.006335,3,0.806,0.0
+superconductivity,regression,Random,TA-TabICLv2,1,14.0,rmse,9.031544,0.008976,3,0.048,100.0
+superconductivity,regression,LOCO,TA-TabICLv2,1,14.0,rmse,9.036523,0.030789,3,1575.465,0.0
+superconductivity,regression,CART,TA-TabICLv2,1,14.0,rmse,9.06016,0.106193,3,1.039,0.0
+superconductivity,regression,RFImportance,TA-TabICLv2,1,14.0,rmse,9.067563,0.099447,3,10.699,0.0
+superconductivity,regression,mRMR,TA-TabICLv2,1,14.0,rmse,9.072237,0.009769,3,4.542,0.0
+superconductivity,regression,SFS,TA-TabICLv2,1,14.0,rmse,9.085496,0.039912,3,3606.473,100.0
+superconductivity,regression,Lasso,TA-TabICLv2,1,14.0,rmse,9.092904,0.064183,3,1.605,0.0
+superconductivity,regression,(R)ReliefF,TA-TabICLv2,1,14.0,rmse,9.09917,0.059058,3,1033.494,0.0
+superconductivity,regression,SBE,TA-TabICLv2,1,14.0,rmse,9.131955,0.071251,3,3607.521,100.0
+superconductivity,regression,F-Test,TA-TabICLv2,1,14.0,rmse,9.185054,0.042747,3,0.025,0.0
+superconductivity,regression,GainRatio,TA-TabICLv2,1,14.0,rmse,9.239858,0.041099,3,3.157,0.0
+superconductivity,regression,LaplacianScore,TA-TabICLv2,1,14.0,rmse,9.98651,0.0617,3,0.215,0.0
+superconductivity,regression,MI,TA-TabICLv2,1,14.0,rmse,10.383455,0.499815,3,3.843,0.0
+superconductivity,regression,RFImportance,TA-TabICLv2,2,27.0,rmse,8.829768,0.015894,3,10.75,0.0
+superconductivity,regression,ElasticNet,TA-TabICLv2,2,27.0,rmse,8.847006,0.020299,3,2.072,0.0
+superconductivity,regression,Lasso,TA-TabICLv2,2,27.0,rmse,8.859749,0.033316,3,1.513,0.0
+superconductivity,regression,LOCO,TA-TabICLv2,2,27.0,rmse,8.862116,0.059623,3,1600.623,0.0
+superconductivity,regression,GainRatio,TA-TabICLv2,2,27.0,rmse,8.867151,0.084294,3,3.164,0.0
+superconductivity,regression,SBE,TA-TabICLv2,2,27.0,rmse,8.871775,0.005386,3,3612.56,100.0
+superconductivity,regression,CART,TA-TabICLv2,2,27.0,rmse,8.879553,0.041672,3,1.008,0.0
+superconductivity,regression,mRMR,TA-TabICLv2,2,27.0,rmse,8.879687,0.042586,3,5.715,0.0
+superconductivity,regression,F-Test,TA-TabICLv2,2,27.0,rmse,8.89651,0.046778,3,0.023,0.0
+superconductivity,regression,(R)ReliefF,TA-TabICLv2,2,27.0,rmse,8.89944,0.041089,3,1026.559,0.0
+superconductivity,regression,Random,TA-TabICLv2,2,27.0,rmse,8.915203,0.017854,3,0.0,100.0
+superconductivity,regression,SFS,TA-TabICLv2,2,27.0,rmse,8.916264,0.026679,3,3605.81,100.0
+superconductivity,regression,MarkovBlanket,TA-TabICLv2,2,27.0,rmse,8.934916,0.014797,3,0.768,0.0
+superconductivity,regression,LaplacianScore,TA-TabICLv2,2,27.0,rmse,8.953492,0.005431,3,0.211,0.0
+superconductivity,regression,MI,TA-TabICLv2,2,27.0,rmse,9.088608,0.053839,3,3.87,0.0
+superconductivity,regression,LOCO,TA-TabICLv2,3,41.0,rmse,8.790432,0.043328,3,1333.804,0.0
+superconductivity,regression,Lasso,TA-TabICLv2,3,41.0,rmse,8.813196,0.034956,3,1.242,0.0
+superconductivity,regression,CART,TA-TabICLv2,3,41.0,rmse,8.815115,0.027322,3,0.995,0.0
+superconductivity,regression,(R)ReliefF,TA-TabICLv2,3,41.0,rmse,8.821298,0.048149,3,1039.646,0.0
+superconductivity,regression,RFImportance,TA-TabICLv2,3,41.0,rmse,8.82592,0.015327,3,10.626,0.0
+superconductivity,regression,ElasticNet,TA-TabICLv2,3,41.0,rmse,8.826435,0.028301,3,2.03,0.0
+superconductivity,regression,mRMR,TA-TabICLv2,3,41.0,rmse,8.84487,0.05696,3,6.571,0.0
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+wine_quality,regression,GainRatio,TA-TabICLv2,2,4.0,rmse,0.669611,0.013472,3,0.181,0.0
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+wine_quality,regression,SFS,TA-TabICLv2,2,4.0,rmse,0.679517,0.007917,3,370.687,0.0
+wine_quality,regression,Random,TA-TabICLv2,2,4.0,rmse,0.69792,0.042417,3,0.0,100.0
+wine_quality,regression,mRMR,TA-TabICLv2,2,4.0,rmse,0.698192,0.010843,3,0.219,0.0
+wine_quality,regression,LOCO,TA-TabICLv2,2,4.0,rmse,0.705221,0.018273,3,85.493,0.0
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+wine_quality,regression,RFImportance,TA-TabICLv2,3,6.0,rmse,0.608327,0.006904,3,0.369,0.0
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+wine_quality,regression,SFS,TA-TabICLv2,3,6.0,rmse,0.619367,0.007769,3,419.928,0.0
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+wine_quality,regression,LaplacianScore,TA-TabICLv2,3,6.0,rmse,0.630302,0.005786,3,0.12,0.0
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+wine_quality,regression,F-Test,TA-TabICLv2,3,6.0,rmse,0.64367,0.00813,3,0.005,0.0
+wine_quality,regression,RFImportance,TA-TabICLv2,4,8.0,rmse,0.596027,0.009916,3,0.38,0.0
+wine_quality,regression,CART,TA-TabICLv2,4,8.0,rmse,0.598121,0.009237,3,0.034,0.0
+wine_quality,regression,MarkovBlanket,TA-TabICLv2,4,8.0,rmse,0.599702,0.009358,3,0.106,0.0
+wine_quality,regression,(R)ReliefF,TA-TabICLv2,4,8.0,rmse,0.59985,0.00825,3,95.452,0.0
+wine_quality,regression,SBE,TA-TabICLv2,4,8.0,rmse,0.602698,0.008135,3,342.755,0.0
+wine_quality,regression,SFS,TA-TabICLv2,4,8.0,rmse,0.602783,0.007016,3,511.491,0.0
+wine_quality,regression,GainRatio,TA-TabICLv2,4,8.0,rmse,0.603145,0.011937,3,0.18,0.0
+wine_quality,regression,LOCO,TA-TabICLv2,4,8.0,rmse,0.603632,0.007949,3,110.068,0.0
+wine_quality,regression,MI,TA-TabICLv2,4,8.0,rmse,0.604321,0.014801,3,0.173,0.0
+wine_quality,regression,mRMR,TA-TabICLv2,4,8.0,rmse,0.607093,0.007556,3,0.237,0.0
+wine_quality,regression,Lasso,TA-TabICLv2,4,8.0,rmse,0.608023,0.002624,3,0.055,0.0
+wine_quality,regression,ElasticNet,TA-TabICLv2,4,8.0,rmse,0.608173,0.002782,3,0.155,0.0
+wine_quality,regression,F-Test,TA-TabICLv2,4,8.0,rmse,0.611209,0.005429,3,0.005,0.0
+wine_quality,regression,LaplacianScore,TA-TabICLv2,4,8.0,rmse,0.61265,0.008771,3,0.113,0.0
+wine_quality,regression,Random,TA-TabICLv2,4,8.0,rmse,0.620196,0.009161,3,0.0,100.0
+wine_quality,regression,(R)ReliefF,TA-TabICLv2,5,10.0,rmse,0.59174,0.010416,3,94.775,0.0
+wine_quality,regression,CART,TA-TabICLv2,5,10.0,rmse,0.591955,0.012523,3,0.034,0.0
+wine_quality,regression,SBE,TA-TabICLv2,5,10.0,rmse,0.593467,0.0117,3,190.66,0.0
+wine_quality,regression,RFImportance,TA-TabICLv2,5,10.0,rmse,0.593617,0.012337,3,0.369,0.0
+wine_quality,regression,GainRatio,TA-TabICLv2,5,10.0,rmse,0.594715,0.011576,3,0.192,0.0
+wine_quality,regression,Lasso,TA-TabICLv2,5,10.0,rmse,0.595093,0.007779,3,0.058,0.0
+wine_quality,regression,ElasticNet,TA-TabICLv2,5,10.0,rmse,0.595212,0.007693,3,0.15,0.0
+wine_quality,regression,MI,TA-TabICLv2,5,10.0,rmse,0.595235,0.015524,3,0.183,0.0
+wine_quality,regression,MarkovBlanket,TA-TabICLv2,5,10.0,rmse,0.59576,0.010973,3,0.081,0.0
+wine_quality,regression,LOCO,TA-TabICLv2,5,10.0,rmse,0.595918,0.012059,3,109.491,0.0
+wine_quality,regression,mRMR,TA-TabICLv2,5,10.0,rmse,0.597828,0.009743,3,0.251,0.0
+wine_quality,regression,SFS,TA-TabICLv2,5,10.0,rmse,0.598973,0.01175,3,434.637,0.0
+wine_quality,regression,LaplacianScore,TA-TabICLv2,5,10.0,rmse,0.599229,0.009715,3,0.115,0.0
+wine_quality,regression,Random,TA-TabICLv2,5,10.0,rmse,0.602437,0.010742,3,0.0,100.0
+wine_quality,regression,F-Test,TA-TabICLv2,5,10.0,rmse,0.603573,0.010813,3,0.005,0.0
diff --git a/data/results_explorer.html b/data/results_explorer.html
new file mode 100644
index 0000000000000000000000000000000000000000..808c498ebc583e6660becd290688312767ad1ba3
--- /dev/null
+++ b/data/results_explorer.html
@@ -0,0 +1,158 @@
+<!doctype html>
+<html lang='en'><head><meta charset='utf-8'><meta name='viewport' content='width=device-width,initial-scale=1'><title>SelectArena: results per dataset, method, model and budget</title><style>
+:root { --fg:#1f2328; --muted:#59636e; --bg:#ffffff; --line:#d1d9e0; --soft:#f6f8fa; --accent:#0072B2; }
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+</style></head><body><div class='filters'><label>Dataset <input type='search' data-filter='dataset' placeholder='e.g. credit' aria-label='Filter datasets'></label><label>Task <select data-filter='task_type'><option value=''>All</option><option value='binary'>binary</option><option value='multiclass'>multiclass</option><option value='regression'>regression</option></select></label><label>Method <select data-filter='method'><option value=''>All</option><option value='MI'>MI</option><option value='SBE'>SBE</option><option value='SFS'>SFS</option><option value='(R)ReliefF'>(R)ReliefF</option><option value='CART'>CART</option><option value='ElasticNet'>ElasticNet</option><option value='F-Test'>F-Test</option><option value='GainRatio'>GainRatio</option><option value='LOCO'>LOCO</option><option value='LaplacianScore'>LaplacianScore</option><option value='Lasso'>Lasso</option><option value='MarkovBlanket'>MarkovBlanket</option><option value='RFImportance'>RFImportance</option><option value='Random'>Random</option><option value='mRMR'>mRMR</option></select></label><label>Model <select data-filter='model'><option value=''>All</option><option value='LightGBM'>LightGBM</option><option value='LinearModel'>LinearModel</option><option value='RandomForest'>RandomForest</option><option value='TA-RealMLP'>TA-RealMLP</option><option value='TA-TabICLv2'>TA-TabICLv2</option></select></label><label>Budget <select data-filter='budget'><option value=''>All</option><option value='1'>1</option><option value='2'>2</option><option value='3'>3</option><option value='4'>4</option><option value='5'>5</option></select></label></div><div class='toolbar'><span class='hint'>Click a column to sort; the CSV holds the filtered rows.</span><button onclick='saExplorerCsv()'>CSV</button></div><div class='wrap'><table id='sa-table'><thead><tr><th class=''>Dataset</th><th class=''>Task</th><th class=''>Method</th><th class=''>Model</th><th class='' title='1 is the smallest budget'>Budget</th><th class='num' title='Number of features kept'>#Features</th><th class=''>Metric</th><th class='num' title='Mean test error over the splits (lower is better)'>Error</th><th class='num'>Error std.</th><th class='num'>Splits</th><th class='num' title='Mean wall-clock time of the selection'>Selection time (s)</th><th class='num' title='Share of splits whose budget was filled at random'>Fallback (%)</th></tr></thead><tbody></tbody></table></div><div class='pager'><span id='sa-count'></span><button id='sa-prev'>Previous</button><button id='sa-next'>Next</button></div><script type='application/json' 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diff --git a/data/subsets/tasks_all/models_all/budgets_0/pareto.csv b/data/subsets/tasks_all/models_all/budgets_0/pareto.csv
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index 0000000000000000000000000000000000000000..780b5ee50bf3ac1554503119b86460ea965d5d0b
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diff --git a/data/subsets/tasks_all/models_ta-tabiclv2/budgets_0/pareto.csv b/data/subsets/tasks_all/models_ta-tabiclv2/budgets_0/pareto.csv
new file mode 100644
index 0000000000000000000000000000000000000000..4e5c633d9574f3ed1e6411e51d35b6d305b719dc
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diff --git a/data/subsets/tasks_all/models_ta-tabiclv2/budgets_all/pareto.csv b/data/subsets/tasks_all/models_ta-tabiclv2/budgets_all/pareto.csv
new file mode 100644
index 0000000000000000000000000000000000000000..3646b442d4b041a8e94f9597226025940083c141
--- /dev/null
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@@ -0,0 +1,16 @@
+method,improvement,time_per_1k,n_datasets,pareto_optimal
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index 0000000000000000000000000000000000000000..e69919a113a61d675cff82b87b0dd20d739d8901
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diff --git a/data/subsets/tasks_binary/models_ta-realmlp/budgets_1/pareto.csv b/data/subsets/tasks_binary/models_ta-realmlp/budgets_1/pareto.csv
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index 0000000000000000000000000000000000000000..60cf15ccff17a305075477eb4ba86c1a1f82f557
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new file mode 100644
index 0000000000000000000000000000000000000000..3514f2e36baf289fd9d01553c9fd387cdf3d755d
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+RFImportance,34.5875,24.0029,49,True
+F-Test,33.9174,1.037,49,True
+MI,33.2232,52.5452,49,False
+MarkovBlanket,31.2112,25.3551,49,False
+CART,30.674,18.1371,49,False
+mRMR,29.866,10.0836,49,False
+GainRatio,27.2387,4.7542,49,False
+ElasticNet,27.1192,3089.124,49,False
+SFS,26.9712,13845.5746,49,False
+(R)ReliefF,25.7653,26692.5809,49,False
+Lasso,25.4253,2680.9691,49,False
+LOCO,17.9837,4823.9829,49,False
+SBE,14.9559,29864.9827,49,False
+Random,0.0,0.003,49,True
+LaplacianScore,-22.1939,4.8481,49,False
diff --git a/data/subsets/tasks_binary/models_ta-tabiclv2/budgets_0/pareto.svg b/data/subsets/tasks_binary/models_ta-tabiclv2/budgets_0/pareto.svg
new file mode 100644
index 0000000000000000000000000000000000000000..39eb2bd8198b93dbb02458c6329090760c5a5463
--- /dev/null
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index 0000000000000000000000000000000000000000..9405751098b7d722537e854c8bfabb14ca4f4570
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index 0000000000000000000000000000000000000000..b53ee5a1b1fb2389e68f1663b7556b308dd60976
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new file mode 100644
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index 0000000000000000000000000000000000000000..0db6c04e531549dd6fe558b4e30091dc761113b1
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diff --git a/data/subsets/tasks_classification/models_ta-tabiclv2/budgets_all/pareto.csv b/data/subsets/tasks_classification/models_ta-tabiclv2/budgets_all/pareto.csv
new file mode 100644
index 0000000000000000000000000000000000000000..4388b403056fce97f81846c95b5c0846d5b2dffe
--- /dev/null
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new file mode 100644
index 0000000000000000000000000000000000000000..3fc30b7c7f89fe7188fd38cfc69a346d6d91e0e6
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index 0000000000000000000000000000000000000000..20a5f0e2a4c99430127455a0702809405a0cd972
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index 0000000000000000000000000000000000000000..404d37cac5c06bb241bcb662aaa685b27a377399
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+method,improvement,time_per_1k,n_datasets,pareto_optimal
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+SFS,7.2013,23288.4862,10,False
+Random,0.0,0.0032,10,True
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new file mode 100644
index 0000000000000000000000000000000000000000..76493eac84f93de2c84990414f3a76e5a9d805f4
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diff --git a/index.html b/index.html
index 3035f3ee4211d8faf2dcab078d7983579c149afb..735dbf7df2fc5747902860bd6582a29046afcddf 100644
--- a/index.html
+++ b/index.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8">
   <meta name="viewport" content="width=device-width, initial-scale=1">
   <!-- Bumped with every data refresh, together with the version history at the bottom. -->
-  <meta name="selectarena-version" content="0.2.1">
+  <meta name="selectarena-version" content="0.3.0">
   <title>SelectArena Leaderboard</title>
   <meta name="description" content="Leaderboard of feature selection methods on real-world tabular data.">
   <link rel="icon" type="image/svg+xml" href="assets/logo.svg">
@@ -36,14 +36,15 @@
   </p>
 
   <div class="tabs" role="tablist" aria-label="Leaderboard sections">
-    <button role="tab" id="tab-leaderboard" aria-controls="panel-leaderboard" aria-selected="true" data-tab="leaderboard">Leaderboard</button>
-    <button role="tab" id="tab-datasets" aria-controls="panel-datasets" aria-selected="false" data-tab="datasets">Per dataset</button>
-    <button role="tab" id="tab-robustness" aria-controls="panel-robustness" aria-selected="false" data-tab="robustness">Stability &amp; validity</button>
+    <button role="tab" id="tab-performance" aria-controls="panel-performance" aria-selected="true" data-tab="performance">Performance</button>
+    <button role="tab" id="tab-stability" aria-controls="panel-stability" aria-selected="false" data-tab="stability">Stability</button>
+    <button role="tab" id="tab-validity" aria-controls="panel-validity" aria-selected="false" data-tab="validity">Validity</button>
+    <button role="tab" id="tab-data" aria-controls="panel-data" aria-selected="false" data-tab="data">Data overview</button>
     <button role="tab" id="tab-about" aria-controls="panel-about" aria-selected="false" data-tab="about">About</button>
   </div>
 
-  <!-- Leaderboard -->
-  <section role="tabpanel" id="panel-leaderboard" aria-labelledby="tab-leaderboard">
+  <!-- Performance -->
+  <section role="tabpanel" id="panel-performance" aria-labelledby="tab-performance">
     <div id="no-results" class="notice" hidden>
       <strong>No results published yet.</strong> The leaderboard fills in once the first benchmark
       results are published from the
@@ -63,6 +64,7 @@
         No leaderboard for this selection: fewer than two methods have results.
       </div>
       <div id="subset-content">
+        <h2>Elo rating</h2>
         <iframe class="frame sa-frame" id="frame-figure" title="Elo rating with stability and validity" style="height:480px"></iframe>
         <p class="caption">
           <strong>Elo rating of each feature selection method, from worst to best.</strong> Elo
@@ -74,37 +76,102 @@
           injected noise features are rejected); darker is better for both. Hatched bars have no
           stability or validity result yet.
         </p>
+        <h2>Performance vs. selection time</h2>
+        <div id="pareto-missing" class="notice" hidden>No Pareto front for this selection.</div>
+        <figure class="plot" id="pareto-figure">
+          <img id="img-pareto" alt="Pareto front of improvement over Random against feature selection time per 1k features">
+          <figcaption class="caption">
+            <strong>Predictive performance against selection time.</strong> Improvement of the
+            downstream error over Random (%, higher is better) against the feature selection time per
+            1,000 features (log scale, left is faster), averaged over budgets, downstream models and
+            splits per dataset, then over datasets. Orange methods are on the Pareto front: no other
+            method is both faster and better. As in the paper, LaplacianScore is left out of the
+            figure (far below Random); its values are in the CSV.
+            <a id="link-pareto-csv" href="#" download>CSV</a> · <a id="link-pareto-svg" href="#" target="_blank">SVG</a>
+          </figcaption>
+        </figure>
+        <h2>Leaderboard</h2>
         <iframe class="frame sa-frame" id="frame-table" title="Leaderboard table" style="height:640px"></iframe>
       </div>
     </div>
   </section>
 
-  <!-- Per dataset -->
-  <section role="tabpanel" id="panel-datasets" aria-labelledby="tab-datasets" hidden>
-    <p class="prose">Mean rank of each method on each dataset, over splits, downstream models and budgets (1 is best).</p>
-    <div id="datasets-missing" class="notice" hidden>No per-dataset results published yet.</div>
-    <iframe class="frame sa-frame" id="frame-datasets" title="Mean rank per dataset" style="height:900px"></iframe>
+  <!-- Stability -->
+  <section role="tabpanel" id="panel-stability" aria-labelledby="tab-stability" hidden>
+    <p class="prose"><strong>Stability</strong> (Nogueira, Sechidis &amp; Brown, JMLR 2018): agreement of the
+      features a method selects across bootstrap resamples of the data, corrected for chance; 1 means
+      identical selections, 0 the agreement of random selections. It comes from runs of the selectors
+      alone, without a downstream model.</p>
+    <div id="stability-missing" class="notice" hidden>No stability results published yet.</div>
+    <div id="stability-content">
+      <div class="controls">
+        <div class="control"><span class="control-label" id="label-stab-tasks">Task type</span>
+          <div class="chips" id="chips-stab-tasks" role="group" aria-labelledby="label-stab-tasks"></div></div>
+        <div class="control"><span class="control-label" id="label-stab-variant">View</span>
+          <div class="chips" id="chips-stab-variant" role="group" aria-labelledby="label-stab-variant"></div></div>
+      </div>
+      <figure class="plot">
+        <img id="img-stability" alt="Lasagna plot of stability per method and events per variable">
+        <figcaption class="caption">
+          <strong>Stability per method and events per variable.</strong> Each column is a dataset,
+          placed by its events per variable (EPV: samples of the smallest class, or all samples for
+          regression, per feature; datasets with few samples per feature are on the right). A cell is
+          the method's mean stability over the budgets; methods are sorted by their mean, most stable
+          on top. <em>Top-3 outlined</em> marks the three most stable methods per EPV; <em>Within 0.03
+          of the best</em> shows only the methods at most 0.03 below the best one.
+          <a id="link-stability-csv" href="#" download>CSV</a> · <a id="link-stability-svg" href="#" target="_blank">SVG</a>
+        </figcaption>
+      </figure>
+      <h2>Mean stability per method and budget</h2>
+      <iframe class="frame sa-frame" id="frame-stability" title="Stability per method" style="height:640px"></iframe>
+    </div>
   </section>
 
-  <!-- Stability & validity -->
-  <section role="tabpanel" id="panel-robustness" aria-labelledby="tab-robustness" hidden>
-    <div class="prose">
-      <p>These metrics come from runs of the selectors alone, without a downstream model.</p>
-      <ul>
-        <li><strong>Stability</strong> (Nogueira, Sechidis &amp; Brown, JMLR 2018): agreement of the
-          selected features across bootstrap resamples of the data, corrected for chance; 1 means
-          identical selections, 0 the agreement of random selections.</li>
-        <li><strong>Validity</strong>: share of the budget filled with original features after synthetic
-          noise features are added (the noise level is the number of noise features per original one).
-          The leaderboard and the figure use the validity at noise level <span id="validity-noise">1.0</span>.</li>
-      </ul>
-    </div>
-    <h2>Stability</h2>
-    <div id="stability-missing" class="notice" hidden>No stability results published yet.</div>
-    <iframe class="frame sa-frame" id="frame-stability" title="Stability per method" style="height:640px"></iframe>
-    <h2>Validity</h2>
+  <!-- Validity -->
+  <section role="tabpanel" id="panel-validity" aria-labelledby="tab-validity" hidden>
+    <p class="prose"><strong>Validity</strong>: share of the budget a method fills with original features
+      after synthetic noise features are added to the data. The noise level is the number of noise
+      features per original one; 1 is best. It comes from runs of the selectors alone, without a
+      downstream model. The leaderboard's Elo figure uses the validity at noise level
+      <span id="validity-noise">1.0</span>.</p>
     <div id="validity-missing" class="notice" hidden>No validity results published yet.</div>
-    <div id="validity-tables"></div>
+    <div id="validity-content">
+      <div class="controls">
+        <div class="control"><span class="control-label" id="label-val-noise">Noise level</span>
+          <div class="chips" id="chips-val-noise" role="group" aria-labelledby="label-val-noise"></div></div>
+        <div class="control"><span class="control-label" id="label-val-tasks">Task type</span>
+          <div class="chips" id="chips-val-tasks" role="group" aria-labelledby="label-val-tasks"></div></div>
+        <div class="control"><span class="control-label" id="label-val-variant">View</span>
+          <div class="chips" id="chips-val-variant" role="group" aria-labelledby="label-val-variant"></div></div>
+      </div>
+      <figure class="plot">
+        <img id="img-validity" alt="Lasagna plot of validity per method and events per variable">
+        <figcaption class="caption">
+          <strong>Validity per method and events per variable.</strong> Each column is a dataset,
+          placed by its events per variable (EPV: samples of the smallest class, or all samples for
+          regression, per original feature). A cell is the method's mean validity over the budgets;
+          methods are sorted by their mean, most valid on top. <em>Top-3 outlined</em> marks the three
+          best methods per EPV; <em>Within 0.03 of the best</em> shows only the methods at most 0.03
+          below the best one.
+          <a id="link-validity-csv" href="#" download>CSV</a> · <a id="link-validity-svg" href="#" target="_blank">SVG</a>
+        </figcaption>
+      </figure>
+      <h2>Mean validity per method and budget</h2>
+      <iframe class="frame sa-frame" id="frame-validity" title="Validity per method" style="height:640px"></iframe>
+    </div>
+  </section>
+
+  <!-- Data overview -->
+  <section role="tabpanel" id="panel-data" aria-labelledby="tab-data" hidden>
+    <p class="prose">The results behind the leaderboard: one row per dataset, feature selection method,
+      downstream model and budget, averaged over the splits (<em>All features</em> is the downstream
+      model without feature selection). Filter, sort and download the rows you need, or get all of
+      them as <a href="data/results.csv" download>results.csv</a>.</p>
+    <div id="data-missing" class="notice" hidden>No results published yet.</div>
+    <iframe class="frame sa-frame" id="frame-results" title="Results per dataset, method, model and budget" style="height:900px"></iframe>
+    <h2>Mean rank per dataset</h2>
+    <p class="prose">Mean rank of each method on each dataset, over splits, downstream models and budgets (1 is best).</p>
+    <iframe class="frame sa-frame" id="frame-datasets" title="Mean rank per dataset" style="height:900px"></iframe>
   </section>
 
   <!-- About -->
@@ -125,7 +192,9 @@
           the best method of the group (0 is best).</li>
         <li><strong>Error vs. all features</strong>: mean ratio of the error to the same model trained on
           all features. Below 1 means the selection improved the model.</li>
-        <li><strong>Stability</strong> and <strong>Validity</strong>: see the <em>Stability &amp; validity</em> tab.</li>
+        <li><strong>Stability</strong> and <strong>Validity</strong>: see their tabs.</li>
+        <li><strong>Pareto front</strong> (Performance tab): improvement of the downstream error over
+          Random against the selection time per 1,000 features, as in the paper.</li>
         <li><strong>Time</strong>: median wall-clock time of the selection. <strong>Fallback</strong>:
           share of runs in which the method ran out of time and random features filled the budget.</li>
       </ul>
@@ -168,6 +237,7 @@
       <summary>Version history (current: <span id="current-version"></span>)</summary>
       <!-- version-history: newest entry first; the top entry's version must equal selectarena-version above. -->
       <ul class="history" id="version-history">
+        <li><strong>2026/11/03 · v0.3.0</strong>: New tabs Performance (Elo, Pareto front of performance vs. selection time, leaderboard), Stability and Validity (the paper's lasagna plots per task type and noise level) and Data overview (results explorer).</li>
         <li><strong>2026/10/14 · v0.2.1</strong>: New logo; datasets named for results that use
           TabArena task ids.</li>
         <li><strong>2026/10/14 · v0.2.0</strong>: Type column follows the paper's taxonomy (search /
diff --git a/smoke_test.py b/smoke_test.py
index b05bf702cdbc9522794f1e3df43f11a9f1723c27..01dd72785c48c49256fbe810ec29b1f479f0e69d 100644
--- a/smoke_test.py
+++ b/smoke_test.py
@@ -1,7 +1,8 @@
 """Check the site against the current ``data/``: ``python smoke_test.py`` (standard library only).
 
-Checks that every subset listed in ``data/meta.json`` has its files, that the leaderboards have one
-row per method with Random at 1000, that the top-level pages exist, that ``data/`` holds nothing
+Checks that every subset listed in ``data/meta.json`` has its files (and its Pareto front when
+listed), that the leaderboards have one row per method with Random at 1000, that the top-level
+pages and the lasagna plots listed in ``meta.json`` exist, that ``data/`` holds nothing
 else, and that the version in ``index.html`` matches the newest version-history entry. For the
 visual check, serve the folder (``python -m http.server 8000``) and open http://localhost:8000.
 """
@@ -29,6 +30,9 @@ TOP_LEVEL = {
     ".gitkeep",
 }
 OPTIONAL = {
+    "lasagna",
+    "results.csv",
+    "results_explorer.html",
     "stability.csv",
     "stability_table.html",
     "validity.csv",
@@ -74,6 +78,10 @@ def main() -> int:
                 errors.append(f"{info['path']}: Random has Elo {random[0]['Elo']}, not 1000")
             if any(r["Category"] == "Other" for r in rows):
                 errors.append(f"{info['path']}: a method has no subgroup in the taxonomy ('Other')")
+        if info.get("pareto"):
+            for name in ("pareto.svg", "pareto.csv"):
+                if not (folder / name).is_file():
+                    errors.append(f"meta.json lists a Pareto front but {info['path']}/{name} is missing")
     if not any(s["tasks"] == s["model"] == s["budget"] == "all" for s in meta["subsets"]):
         errors.append("the default subset (all, all, all) is not published")
 
@@ -89,6 +97,19 @@ def main() -> int:
     for table in meta.get("validity_tables", []):
         if not (DATA / table["path"]).is_file():
             errors.append(f"missing data/{table['path']} (validity at noise {table['noise']})")
+    if meta.get("has_results_explorer") and not (DATA / "results_explorer.html").is_file():
+        errors.append("meta.json says has_results_explorer but data/results_explorer.html is missing")
+    lasagna = meta.get("lasagna") or {}
+    variants = lasagna.get("variants", [])
+    folders = []
+    if lasagna.get("stability"):
+        folders += [(lasagna["stability"]["path"], lasagna["stability"]["task_types"])]
+    folders += [(level["path"], level["task_types"]) for level in lasagna.get("validity", [])]
+    for path, task_types in folders:
+        for tasks in task_types:
+            for name in [f"{v}.svg" for v in variants] + ["per_epv.csv"]:
+                if not (DATA / path / f"tasks_{tasks}" / name).is_file():
+                    errors.append(f"missing data/{path}/tasks_{tasks}/{name} (listed in meta.json lasagna)")
     validity_pages = {p.name for p in DATA.glob("validity_noise_*_table.html")}
     unexpected = {p.name for p in DATA.iterdir()} - TOP_LEVEL - OPTIONAL - validity_pages
     if unexpected: