metadata

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
      - 15
      - 24
    Latitude:
      - 34.23
      - 36.84
      - 34.04
    Longitude:
      - -117.41
      - -119.77
      - -118.3
    MedInc:
      - 6.1426
      - 5.3886
      - 1.7109
    Population:
      - 439
      - 848
      - 1757

Model description

[More Information Needed]

Intended uses & limitations

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Training Procedure

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Hyperparameters

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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

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How to Get Started with the Model

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Model Card Authors

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