Run
10591911

Run 10591911

Task 361294 (Supervised Regression) medical_charges Uploaded 14-03-2023 by PAVAN KUMAR PERUGU
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Flow

sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transfo rmer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=skle arn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.S tandardScaler),nominal=sklearn.pipeline.Pipeline(onehotencoder=sklearn.prep rocessing._encoders.OneHotEncoder)),decisiontreeregressor=sklearn.tree._cla sses.DecisionTreeRegressor)(4)Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement `fit` and `transform` methods. The final estimator only needs to implement `fit`. The transformers in the pipeline can be cached using ``memory`` argument. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a `'__'`, as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting it to `'passthrough'` or `None`.
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),decisiontreeregressor=sklearn.tree._classes.DecisionTreeRegressor)(4)_memorynull
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),decisiontreeregressor=sklearn.tree._classes.DecisionTreeRegressor)(4)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "decisiontreeregressor", "step_name": "decisiontreeregressor"}}]
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),decisiontreeregressor=sklearn.tree._classes.DecisionTreeRegressor)(4)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(4)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(4)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(4)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(4)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(4)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": [0, 1, 2]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": []}}]
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(4)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(4)_verbose_feature_names_outtrue
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(4)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(4)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}]
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(4)_verbosefalse
sklearn.impute._base.SimpleImputer(41)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(41)_copytrue
sklearn.impute._base.SimpleImputer(41)_fill_valuenull
sklearn.impute._base.SimpleImputer(41)_keep_empty_featuresfalse
sklearn.impute._base.SimpleImputer(41)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(41)_strategy"mean"
sklearn.impute._base.SimpleImputer(41)_verbose"deprecated"
sklearn.preprocessing._data.StandardScaler(14)_copytrue
sklearn.preprocessing._data.StandardScaler(14)_with_meantrue
sklearn.preprocessing._data.StandardScaler(14)_with_stdtrue
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(13)_memorynull
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(13)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}]
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(13)_verbosefalse
sklearn.preprocessing._encoders.OneHotEncoder(40)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(40)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(40)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(40)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(40)_max_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(40)_min_frequencynull
sklearn.preprocessing._encoders.OneHotEncoder(40)_sparse"deprecated"
sklearn.preprocessing._encoders.OneHotEncoder(40)_sparse_outputtrue
sklearn.tree._classes.DecisionTreeRegressor(6)_ccp_alpha0.0
sklearn.tree._classes.DecisionTreeRegressor(6)_criterion"friedman_mse"
sklearn.tree._classes.DecisionTreeRegressor(6)_max_depthnull
sklearn.tree._classes.DecisionTreeRegressor(6)_max_features1.0
sklearn.tree._classes.DecisionTreeRegressor(6)_max_leaf_nodesnull
sklearn.tree._classes.DecisionTreeRegressor(6)_min_impurity_decrease0.0
sklearn.tree._classes.DecisionTreeRegressor(6)_min_samples_leaf6
sklearn.tree._classes.DecisionTreeRegressor(6)_min_samples_split10
sklearn.tree._classes.DecisionTreeRegressor(6)_min_weight_fraction_leaf0.0
sklearn.tree._classes.DecisionTreeRegressor(6)_random_state21687
sklearn.tree._classes.DecisionTreeRegressor(6)_splitter"best"

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

7 Evaluation measures

Mean prior absolute error
0.4517 ± 0.0024
Cross-validation details (10-fold Crossvalidation)
Number of instances
163065
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.5664 ± 0.0029
Cross-validation details (10-fold Crossvalidation)