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Model description

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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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