--- library_name: sklearn tags: - sklearn - skops - tabular-regression model_format: skops model_file: model.skops widget: structuredData: AveBedrms: - 0.9290780141843972 - 0.9458483754512635 - 1.087360594795539 AveOccup: - 3.1134751773049647 - 3.0613718411552346 - 3.2657992565055762 AveRooms: - 6.304964539007092 - 6.945848375451264 - 3.8884758364312266 HouseAge: - 17.0 - 15.0 - 24.0 Latitude: - 34.23 - 36.84 - 34.04 Longitude: - -117.41 - -119.77 - -118.3 MedInc: - 6.1426 - 5.3886 - 1.7109 Population: - 439.0 - 848.0 - 1757.0 --- # Model description [More Information Needed] ## Intended uses & limitations [More Information Needed] ## Training Procedure [More Information Needed] ### Hyperparameters
Click to expand | Hyperparameter | Value | |-----------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | cv | | | estimators | [('knn@5', Pipeline(steps=[('select_cols',
ColumnTransformer(transformers=[('long_and_lat', 'passthrough',
['Longitude', 'Latitude'])])),
('knn', KNeighborsRegressor())]))] | | final_estimator__alpha | 0.9 | | final_estimator__ccp_alpha | 0.0 | | final_estimator__criterion | friedman_mse | | final_estimator__init | | | final_estimator__learning_rate | 0.1 | | final_estimator__loss | squared_error | | final_estimator__max_depth | 3 | | final_estimator__max_features | | | final_estimator__max_leaf_nodes | | | final_estimator__min_impurity_decrease | 0.0 | | final_estimator__min_samples_leaf | 1 | | final_estimator__min_samples_split | 2 | | final_estimator__min_weight_fraction_leaf | 0.0 | | final_estimator__n_estimators | 500 | | final_estimator__n_iter_no_change | | | final_estimator__random_state | 0 | | final_estimator__subsample | 1.0 | | final_estimator__tol | 0.0001 | | final_estimator__validation_fraction | 0.1 | | final_estimator__verbose | 0 | | final_estimator__warm_start | False | | final_estimator | GradientBoostingRegressor(n_estimators=500, random_state=0) | | n_jobs | | | passthrough | True | | verbose | 0 | | knn@5 | Pipeline(steps=[('select_cols',
ColumnTransformer(transformers=[('long_and_lat', 'passthrough',
['Longitude', 'Latitude'])])),
('knn', KNeighborsRegressor())]) | | knn@5__memory | | | knn@5__steps | [('select_cols', ColumnTransformer(transformers=[('long_and_lat', 'passthrough',
['Longitude', 'Latitude'])])), ('knn', KNeighborsRegressor())] | | knn@5__verbose | False | | knn@5__select_cols | ColumnTransformer(transformers=[('long_and_lat', 'passthrough',
['Longitude', 'Latitude'])]) | | knn@5__knn | KNeighborsRegressor() | | knn@5__select_cols__n_jobs | | | knn@5__select_cols__remainder | drop | | knn@5__select_cols__sparse_threshold | 0.3 | | knn@5__select_cols__transformer_weights | | | knn@5__select_cols__transformers | [('long_and_lat', 'passthrough', ['Longitude', 'Latitude'])] | | knn@5__select_cols__verbose | False | | knn@5__select_cols__verbose_feature_names_out | True | | knn@5__select_cols__long_and_lat | passthrough | | knn@5__knn__algorithm | auto | | knn@5__knn__leaf_size | 30 | | knn@5__knn__metric | minkowski | | knn@5__knn__metric_params | | | knn@5__knn__n_jobs | | | knn@5__knn__n_neighbors | 5 | | knn@5__knn__p | 2 | | knn@5__knn__weights | uniform |
### Model Plot 
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)
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## 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] ```