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--- |
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library_name: sklearn |
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tags: |
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- sklearn |
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- skops |
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- tabular-regression |
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model_format: skops |
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model_file: model.skops |
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widget: |
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structuredData: |
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AveBedrms: |
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- 0.9290780141843972 |
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- 0.9458483754512635 |
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- 1.087360594795539 |
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AveOccup: |
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- 3.1134751773049647 |
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- 3.0613718411552346 |
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- 3.2657992565055762 |
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AveRooms: |
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- 6.304964539007092 |
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- 6.945848375451264 |
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- 3.8884758364312266 |
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HouseAge: |
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- 17.0 |
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- 15.0 |
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- 24.0 |
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Latitude: |
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- 34.23 |
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- 36.84 |
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- 34.04 |
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Longitude: |
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- -117.41 |
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- -119.77 |
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- -118.3 |
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MedInc: |
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- 6.1426 |
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- 5.3886 |
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- 1.7109 |
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Population: |
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- 439.0 |
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- 848.0 |
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- 1757.0 |
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--- |
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# Model description |
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Gradient boosting regressor trained on California Housing dataset |
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The model is a gradient boosting regressor from sklearn. On top of the standard |
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features, it contains predictions from a KNN models. These predictions are calculated |
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out of fold, then added on top of the existing features. These features are really |
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helpful for decision tree-based models, since those cannot easily learn from geospatial |
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data. |
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## Intended uses & limitations |
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This model is meant for demonstration purposes |
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## Training Procedure |
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### Hyperparameters |
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The model is trained with below hyperparameters. |
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<details> |
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<summary> Click to expand </summary> |
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| Hyperparameter | Value | |
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|-----------------------------------------------|--------------------------------------------------------------| |
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| cv | | |
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| estimators | [('knn@5', Pipeline(steps=[('select_cols',<br /> ColumnTransformer(transformers=[('long_and_lat', 'passthrough',<br /> ['Longitude', 'Latitude'])])),<br /> ('knn', KNeighborsRegressor())]))] | |
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| final_estimator__alpha | 0.9 | |
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| final_estimator__ccp_alpha | 0.0 | |
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| final_estimator__criterion | friedman_mse | |
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| final_estimator__init | | |
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| final_estimator__learning_rate | 0.1 | |
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| final_estimator__loss | squared_error | |
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| final_estimator__max_depth | 3 | |
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| final_estimator__max_features | | |
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| final_estimator__max_leaf_nodes | | |
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| final_estimator__min_impurity_decrease | 0.0 | |
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| final_estimator__min_samples_leaf | 1 | |
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| final_estimator__min_samples_split | 2 | |
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| final_estimator__min_weight_fraction_leaf | 0.0 | |
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| final_estimator__n_estimators | 500 | |
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| final_estimator__n_iter_no_change | | |
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| final_estimator__random_state | 0 | |
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| final_estimator__subsample | 1.0 | |
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| final_estimator__tol | 0.0001 | |
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| final_estimator__validation_fraction | 0.1 | |
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| final_estimator__verbose | 0 | |
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| final_estimator__warm_start | False | |
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| final_estimator | GradientBoostingRegressor(n_estimators=500, random_state=0) | |
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| n_jobs | | |
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| passthrough | True | |
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| verbose | 0 | |
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| knn@5 | Pipeline(steps=[('select_cols',<br /> ColumnTransformer(transformers=[('long_and_lat', 'passthrough',<br /> ['Longitude', 'Latitude'])])),<br /> ('knn', KNeighborsRegressor())]) | |
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| knn@5__memory | | |
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| knn@5__steps | [('select_cols', ColumnTransformer(transformers=[('long_and_lat', 'passthrough',<br /> ['Longitude', 'Latitude'])])), ('knn', KNeighborsRegressor())] | |
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| knn@5__verbose | False | |
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| knn@5__select_cols | ColumnTransformer(transformers=[('long_and_lat', 'passthrough',<br /> ['Longitude', 'Latitude'])]) | |
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| knn@5__knn | KNeighborsRegressor() | |
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| knn@5__select_cols__n_jobs | | |
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| knn@5__select_cols__remainder | drop | |
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| knn@5__select_cols__sparse_threshold | 0.3 | |
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| knn@5__select_cols__transformer_weights | | |
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| knn@5__select_cols__transformers | [('long_and_lat', 'passthrough', ['Longitude', 'Latitude'])] | |
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| knn@5__select_cols__verbose | False | |
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| knn@5__select_cols__verbose_feature_names_out | True | |
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| knn@5__select_cols__long_and_lat | passthrough | |
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| knn@5__knn__algorithm | auto | |
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| knn@5__knn__leaf_size | 30 | |
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| knn@5__knn__metric | minkowski | |
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| knn@5__knn__metric_params | | |
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| knn@5__knn__n_jobs | | |
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| knn@5__knn__n_neighbors | 5 | |
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| knn@5__knn__p | 2 | |
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| knn@5__knn__weights | uniform | |
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</details> |
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### Model Plot |
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The model plot is below. |
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<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 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-13 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 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-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 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-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 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-13 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-13" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>StackingRegressor(estimators=[('knn@5',Pipeline(steps=[('select_cols',ColumnTransformer(transformers=[('long_and_lat','passthrough',['Longitude','Latitude'])])),('knn',KNeighborsRegressor())]))],final_estimator=GradientBoostingRegressor(n_estimators=500,random_state=0),passthrough=True)</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-41" type="checkbox" ><label for="sk-estimator-id-41" class="sk-toggleable__label sk-toggleable__label-arrow">StackingRegressor</label><div class="sk-toggleable__content"><pre>StackingRegressor(estimators=[('knn@5',Pipeline(steps=[('select_cols',ColumnTransformer(transformers=[('long_and_lat','passthrough',['Longitude','Latitude'])])),('knn',KNeighborsRegressor())]))],final_estimator=GradientBoostingRegressor(n_estimators=500,random_state=0),passthrough=True)</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><label>knn@5</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><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-42" type="checkbox" ><label for="sk-estimator-id-42" class="sk-toggleable__label sk-toggleable__label-arrow">select_cols: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[('long_and_lat', 'passthrough',['Longitude', 'Latitude'])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-43" type="checkbox" ><label for="sk-estimator-id-43" class="sk-toggleable__label sk-toggleable__label-arrow">long_and_lat</label><div class="sk-toggleable__content"><pre>['Longitude', 'Latitude']</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-44" type="checkbox" ><label for="sk-estimator-id-44" class="sk-toggleable__label sk-toggleable__label-arrow">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</pre></div></div></div></div></div></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-45" type="checkbox" ><label for="sk-estimator-id-45" class="sk-toggleable__label sk-toggleable__label-arrow">KNeighborsRegressor</label><div class="sk-toggleable__content"><pre>KNeighborsRegressor()</pre></div></div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><label>final_estimator</label></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-46" type="checkbox" ><label for="sk-estimator-id-46" class="sk-toggleable__label sk-toggleable__label-arrow">GradientBoostingRegressor</label><div class="sk-toggleable__content"><pre>GradientBoostingRegressor(n_estimators=500, random_state=0)</pre></div></div></div></div></div></div></div></div></div></div></div></div> |
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## Evaluation Results |
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Metrics are calculated on the test set |
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| Metric | Value | |
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|-------------------------|--------------| |
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| Root mean squared error | 44273.5 | |
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| Mean absolute error | 30079.9 | |
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| R² | 0.805954 | |
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## Dataset description |
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California Housing dataset |
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-------------------------- |
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**Data Set Characteristics:** |
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:Number of Instances: 20640 |
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:Number of Attributes: 8 numeric, predictive attributes and the target |
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:Attribute Information: |
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- MedInc median income in block group |
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- HouseAge median house age in block group |
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- AveRooms average number of rooms per household |
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- AveBedrms average number of bedrooms per household |
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- Population block group population |
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- AveOccup average number of household members |
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- Latitude block group latitude |
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- Longitude block group longitude |
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:Missing Attribute Values: None |
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This dataset was obtained from the StatLib repository. |
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https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html |
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The target variable is the median house value for California districts, |
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expressed in hundreds of thousands of dollars ($100,000). |
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This dataset was derived from the 1990 U.S. census, using one row per census |
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block group. A block group is the smallest geographical unit for which the U.S. |
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Census Bureau publishes sample data (a block group typically has a population |
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of 600 to 3,000 people). |
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An household is a group of people residing within a home. Since the average |
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number of rooms and bedrooms in this dataset are provided per household, these |
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columns may take surpinsingly large values for block groups with few households |
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and many empty houses, such as vacation resorts. |
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It can be downloaded/loaded using the |
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:func:`sklearn.datasets.fetch_california_housing` function. |
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.. topic:: References |
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- Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions, |
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Statistics and Probability Letters, 33 (1997) 291-297 |
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### Data distribution |
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<details> |
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<summary> Click to expand </summary> |
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</details> |
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# How to Get Started with the Model |
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Run the code below to load the model |
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```python |
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import json |
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import pandas as pd |
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import skops.io as sio |
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model = sio.load("model.skops") |
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with open("config.json") as f: |
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config = json.load(f) |
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model.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"])) |
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``` |
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# Model Card Authors |
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Benjamin Bossan |
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# Model Card Contact |
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benjamin@huggingface.co |
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# Permutation Importances |
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