| --- |
| license: mit |
| library_name: sklearn |
| tags: |
| - sklearn |
| - skops |
| - tabular-classification |
| model_format: skops |
| model_file: examplej.skops |
| widget: |
| structuredData: |
| 'Unnamed: 32': |
| - .nan |
| - .nan |
| - .nan |
| area_mean: |
| - 481.9 |
| - 1130.0 |
| - 748.9 |
| area_se: |
| - 30.29 |
| - 96.05 |
| - 48.31 |
| area_worst: |
| - 677.9 |
| - 1866.0 |
| - 1156.0 |
| compactness_mean: |
| - 0.1058 |
| - 0.1029 |
| - 0.1223 |
| compactness_se: |
| - 0.01911 |
| - 0.01652 |
| - 0.01484 |
| compactness_worst: |
| - 0.2378 |
| - 0.2336 |
| - 0.2394 |
| concave points_mean: |
| - 0.03821 |
| - 0.07951 |
| - 0.08087 |
| concave points_se: |
| - 0.01037 |
| - 0.0137 |
| - 0.01093 |
| concave points_worst: |
| - 0.1015 |
| - 0.1789 |
| - 0.1514 |
| concavity_mean: |
| - 0.08005 |
| - 0.108 |
| - 0.1466 |
| concavity_se: |
| - 0.02701 |
| - 0.02269 |
| - 0.02813 |
| concavity_worst: |
| - 0.2671 |
| - 0.2687 |
| - 0.3791 |
| fractal_dimension_mean: |
| - 0.06373 |
| - 0.05461 |
| - 0.05796 |
| fractal_dimension_se: |
| - 0.003586 |
| - 0.001698 |
| - 0.002461 |
| fractal_dimension_worst: |
| - 0.0875 |
| - 0.06589 |
| - 0.08019 |
| id: |
| - 87930 |
| - 859575 |
| - 8670 |
| perimeter_mean: |
| - 81.09 |
| - 123.6 |
| - 101.7 |
| perimeter_se: |
| - 2.497 |
| - 5.486 |
| - 3.094 |
| perimeter_worst: |
| - 96.05 |
| - 165.9 |
| - 124.9 |
| radius_mean: |
| - 12.47 |
| - 18.94 |
| - 15.46 |
| radius_se: |
| - 0.3961 |
| - 0.7888 |
| - 0.4743 |
| radius_worst: |
| - 14.97 |
| - 24.86 |
| - 19.26 |
| smoothness_mean: |
| - 0.09965 |
| - 0.09009 |
| - 0.1092 |
| smoothness_se: |
| - 0.006953 |
| - 0.004444 |
| - 0.00624 |
| smoothness_worst: |
| - 0.1426 |
| - 0.1193 |
| - 0.1546 |
| symmetry_mean: |
| - 0.1925 |
| - 0.1582 |
| - 0.1931 |
| symmetry_se: |
| - 0.01782 |
| - 0.01386 |
| - 0.01397 |
| symmetry_worst: |
| - 0.3014 |
| - 0.2551 |
| - 0.2837 |
| texture_mean: |
| - 18.6 |
| - 21.31 |
| - 19.48 |
| texture_se: |
| - 1.044 |
| - 0.7975 |
| - 0.7859 |
| texture_worst: |
| - 24.64 |
| - 26.58 |
| - 26.0 |
| --- |
| |
| # Model description |
|
|
| [More Information Needed] |
|
|
| ## Intended uses & limitations |
|
|
| This model is not ready to be used in production (J). |
|
|
| ## Training Procedure |
|
|
| ### Hyperparameters |
|
|
| The model is trained with below hyperparameters. |
|
|
| <details> |
| <summary> Click to expand </summary> |
|
|
| | Hyperparameter | Value | |
| |------------------------------|-----------------------------------------------------------------------------------------------| |
| | memory | | |
| | steps | [('imputer', SimpleImputer()), ('scaler', StandardScaler()), ('model', LogisticRegression())] | |
| | verbose | False | |
| | imputer | SimpleImputer() | |
| | scaler | StandardScaler() | |
| | model | LogisticRegression() | |
| | imputer__add_indicator | False | |
| | imputer__copy | True | |
| | imputer__fill_value | | |
| | imputer__keep_empty_features | False | |
| | imputer__missing_values | nan | |
| | imputer__strategy | mean | |
| | imputer__verbose | deprecated | |
| | 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-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-container-id-6 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-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 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;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 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-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-6" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', StandardScaler()),('model', LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</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="sk-estimator-id-21" type="checkbox" ><label for="sk-estimator-id-21" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('imputer', SimpleImputer()), ('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="sk-estimator-id-22" type="checkbox" ><label for="sk-estimator-id-22" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-23" type="checkbox" ><label for="sk-estimator-id-23" 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="sk-estimator-id-24" type="checkbox" ><label for="sk-estimator-id-24" 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 |
| |
| [More Information Needed] |
| |
| # How to Get Started with the Model |
| |
| [More Information Needed] |
| |
| # Model Card Authors |
| |
| This model card is written by following authors: |
| |
| [More Information Needed] |
| |
| # Model Card Contact |
| |
| You can contact the model card authors through following channels: |
| [More Information Needed] |
| |
| # Citation |
| |
| Below you can find information related to citation. |
| |
| **BibTeX:** |
| ``` |
| [More Information Needed] |
| ``` |
| |
