Run
10591856

Run 10591856

Task 52948 (Supervised Regression) liver-disorders Uploaded 12-02-2023 by Sharath Kumar Reddy Alijarla
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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)(2)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.impute._base.SimpleImputer(38)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(38)_copytrue
sklearn.impute._base.SimpleImputer(38)_fill_valuenull
sklearn.impute._base.SimpleImputer(38)_keep_empty_featuresfalse
sklearn.impute._base.SimpleImputer(38)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(38)_strategy"mean"
sklearn.impute._base.SimpleImputer(38)_verbose"deprecated"
sklearn.tree._classes.DecisionTreeRegressor(3)_ccp_alpha0.0
sklearn.tree._classes.DecisionTreeRegressor(3)_criterion"friedman_mse"
sklearn.tree._classes.DecisionTreeRegressor(3)_max_depthnull
sklearn.tree._classes.DecisionTreeRegressor(3)_max_features1.0
sklearn.tree._classes.DecisionTreeRegressor(3)_max_leaf_nodesnull
sklearn.tree._classes.DecisionTreeRegressor(3)_min_impurity_decrease0.0
sklearn.tree._classes.DecisionTreeRegressor(3)_min_samples_leaf16
sklearn.tree._classes.DecisionTreeRegressor(3)_min_samples_split3
sklearn.tree._classes.DecisionTreeRegressor(3)_min_weight_fraction_leaf0.0
sklearn.tree._classes.DecisionTreeRegressor(3)_random_state20430
sklearn.tree._classes.DecisionTreeRegressor(3)_splitter"best"
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)(2)_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)(2)_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)(2)_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))(2)_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))(2)_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))(2)_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))(2)_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))(2)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": [0, 1, 2, 3, 4]}}, {"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))(2)_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))(2)_verbose_feature_names_outtrue
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(2)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(2)_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)(2)_verbosefalse
sklearn.preprocessing._data.StandardScaler(12)_copytrue
sklearn.preprocessing._data.StandardScaler(12)_with_meantrue
sklearn.preprocessing._data.StandardScaler(12)_with_stdtrue
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(11)_memorynull
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(11)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}]
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(11)_verbosefalse
sklearn.preprocessing._encoders.OneHotEncoder(37)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(37)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(37)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(37)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(37)_max_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(37)_min_frequencynull
sklearn.preprocessing._encoders.OneHotEncoder(37)_sparse"deprecated"
sklearn.preprocessing._encoders.OneHotEncoder(37)_sparse_outputtrue

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
2.623 ± 0.3396
Cross-validation details (10-fold Crossvalidation)
Number of instances
345
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
3.333 ± 0.5467
Cross-validation details (10-fold Crossvalidation)