| --- |
| library_name: sklearn |
| tags: |
| - sklearn |
| - skops |
| - tabular-classification |
| model_file: model.pkl |
| widget: |
| structuredData: |
| area_mean: |
| - 407.4 |
| - 1335.0 |
| - 428.0 |
| area_se: |
| - 26.99 |
| - 77.02 |
| - 17.12 |
| area_worst: |
| - 508.9 |
| - 1946.0 |
| - 546.3 |
| compactness_mean: |
| - 0.05991 |
| - 0.1076 |
| - 0.069 |
| compactness_se: |
| - 0.01065 |
| - 0.01895 |
| - 0.01727 |
| compactness_worst: |
| - 0.1049 |
| - 0.3055 |
| - 0.188 |
| concave points_mean: |
| - 0.02069 |
| - 0.08941 |
| - 0.01393 |
| concave points_se: |
| - 0.009175 |
| - 0.01232 |
| - 0.006747 |
| concave points_worst: |
| - 0.06544 |
| - 0.2112 |
| - 0.06913 |
| concavity_mean: |
| - 0.02638 |
| - 0.1527 |
| - 0.02669 |
| concavity_se: |
| - 0.01245 |
| - 0.02681 |
| - 0.02045 |
| concavity_worst: |
| - 0.08105 |
| - 0.4159 |
| - 0.1471 |
| fractal_dimension_mean: |
| - 0.05934 |
| - 0.05478 |
| - 0.06057 |
| fractal_dimension_se: |
| - 0.001461 |
| - 0.001711 |
| - 0.002922 |
| fractal_dimension_worst: |
| - 0.06487 |
| - 0.07055 |
| - 0.07993 |
| perimeter_mean: |
| - 73.28 |
| - 134.8 |
| - 75.51 |
| perimeter_se: |
| - 2.684 |
| - 4.119 |
| - 1.444 |
| perimeter_worst: |
| - 83.12 |
| - 166.8 |
| - 85.22 |
| radius_mean: |
| - 11.5 |
| - 20.64 |
| - 11.84 |
| radius_se: |
| - 0.3927 |
| - 0.6137 |
| - 0.2222 |
| radius_worst: |
| - 12.97 |
| - 25.37 |
| - 13.3 |
| smoothness_mean: |
| - 0.09345 |
| - 0.09446 |
| - 0.08871 |
| smoothness_se: |
| - 0.00638 |
| - 0.006211 |
| - 0.005517 |
| smoothness_worst: |
| - 0.1183 |
| - 0.1562 |
| - 0.128 |
| symmetry_mean: |
| - 0.1834 |
| - 0.1571 |
| - 0.1533 |
| symmetry_se: |
| - 0.02292 |
| - 0.01276 |
| - 0.01616 |
| symmetry_worst: |
| - 0.274 |
| - 0.2689 |
| - 0.2535 |
| texture_mean: |
| - 18.45 |
| - 17.35 |
| - 18.94 |
| texture_se: |
| - 0.8429 |
| - 0.6575 |
| - 0.8652 |
| texture_worst: |
| - 22.46 |
| - 23.17 |
| - 24.99 |
| --- |
| |
| # Model description |
|
|
| This is a Logistic Regression trained on breast cancer dataset. |
|
|
| ## Intended uses & limitations |
|
|
| This model is trained for educational purposes. |
|
|
| ## Training Procedure |
|
|
| ### Hyperparameters |
|
|
| The model is trained with below hyperparameters. |
|
|
| <details> |
| <summary> Click to expand </summary> |
|
|
| | Hyperparameter | Value | |
| |--------------------------|-----------------------------------------------------------------| |
| | memory | | |
| | steps | [('scaler', StandardScaler()), ('model', LogisticRegression())] | |
| | verbose | False | |
| | scaler | StandardScaler() | |
| | model | LogisticRegression() | |
| | scaler__copy | True | |
| | scaler__with_mean | True | |
| | scaler__with_std | True | |
| | model__C | 1.0 | |
| | model__class_weight | | |
| | model__dual | False | |
| | model__fit_intercept | True | |
| | model__intercept_scaling | 1 | |
| | model__l1_ratio | | |
| | model__max_iter | 100 | |
| | model__multi_class | auto | |
| | model__n_jobs | | |
| | model__penalty | l2 | |
| | model__random_state | | |
| | model__solver | lbfgs | |
| | model__tol | 0.0001 | |
| | model__verbose | 0 | |
| | model__warm_start | False | |
| |
| </details> |
| |
| ### Model Plot |
| |
| The model plot is below. |
| |
| <style>#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 {color: black;background-color: white;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 pre{padding: 0;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-toggleable {background-color: white;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-estimator:hover {background-color: #d4ebff;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-item {z-index: 1;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-parallel-item:only-child::after {width: 0;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-text-repr-fallback {display: none;}</style><div id="sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('scaler', StandardScaler()), ('model', LogisticRegression())])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="76a688ab-e260-4cf7-a9f2-bf77900be27c" type="checkbox" ><label for="76a688ab-e260-4cf7-a9f2-bf77900be27c" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('scaler', StandardScaler()), ('model', LogisticRegression())])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="6a4fcd10-6b63-40a6-a848-13717b9f7c82" type="checkbox" ><label for="6a4fcd10-6b63-40a6-a848-13717b9f7c82" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="974bd93d-19db-4a61-b7ff-66d07e5bbadb" type="checkbox" ><label for="974bd93d-19db-4a61-b7ff-66d07e5bbadb" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div> |
| |
| ## Evaluation Results |
| |
| You can find the details about evaluation process and the evaluation results. |
| |
| |
| |
| | Metric | Value | |
| |----------|----------| |
| | accuracy | 0.965035 | |
| | f1 score | 0.965035 | |
| |
| # How to Get Started with the Model |
| |
| Use the code below to get started with the model. |
| |
| ```python |
| import joblib |
| import json |
| import pandas as pd |
| clf = joblib.load(model.pkl) |
| with open("config.json") as f: |
| config = json.load(f) |
| clf.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"])) |
| ``` |
| |
| |
| # Additional Content |
| |
| ## Confusion Matrix |
| |
|  |
