Run 10591679

Task 96 (Learning Curve) diabetes Uploaded 24-10-2022 by Lucas Maertens
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sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer .ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncode r,continuous=sklearn.impute._base.SimpleImputer),Classifier=sklearn.ensembl e._forest.RandomForestClassifier)(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.impute._base.SimpleImputer(34)_add_indicator	false
sklearn.impute._base.SimpleImputer(34)_copy	true
sklearn.impute._base.SimpleImputer(34)_fill_value	null
sklearn.impute._base.SimpleImputer(34)_missing_values	NaN
sklearn.impute._base.SimpleImputer(34)_strategy	"median"
sklearn.impute._base.SimpleImputer(34)_verbose	"deprecated"
sklearn.ensemble._forest.RandomForestClassifier(17)_bootstrap	true
sklearn.ensemble._forest.RandomForestClassifier(17)_ccp_alpha	0.0
sklearn.ensemble._forest.RandomForestClassifier(17)_class_weight	null
sklearn.ensemble._forest.RandomForestClassifier(17)_criterion	"gini"
sklearn.ensemble._forest.RandomForestClassifier(17)_max_depth	null
sklearn.ensemble._forest.RandomForestClassifier(17)_max_features	"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(17)_max_leaf_nodes	null
sklearn.ensemble._forest.RandomForestClassifier(17)_max_samples	null
sklearn.ensemble._forest.RandomForestClassifier(17)_min_impurity_decrease	0.0
sklearn.ensemble._forest.RandomForestClassifier(17)_min_samples_leaf	1
sklearn.ensemble._forest.RandomForestClassifier(17)_min_samples_split	2
sklearn.ensemble._forest.RandomForestClassifier(17)_min_weight_fraction_leaf	0.0
sklearn.ensemble._forest.RandomForestClassifier(17)_n_estimators	10
sklearn.ensemble._forest.RandomForestClassifier(17)_n_jobs	null
sklearn.ensemble._forest.RandomForestClassifier(17)_oob_score	false
sklearn.ensemble._forest.RandomForestClassifier(17)_random_state	24000
sklearn.ensemble._forest.RandomForestClassifier(17)_verbose	0
sklearn.ensemble._forest.RandomForestClassifier(17)_warm_start	false
sklearn.preprocessing._encoders.OneHotEncoder(33)_categories	"auto"
sklearn.preprocessing._encoders.OneHotEncoder(33)_drop	null
sklearn.preprocessing._encoders.OneHotEncoder(33)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(33)_handle_unknown	"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(33)_max_categories	null
sklearn.preprocessing._encoders.OneHotEncoder(33)_min_frequency	null
sklearn.preprocessing._encoders.OneHotEncoder(33)_sparse	false
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer),Classifier=sklearn.ensemble._forest.RandomForestClassifier)(4)_memory	null
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer),Classifier=sklearn.ensemble._forest.RandomForestClassifier)(4)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "Preprocessing", "step_name": "Preprocessing"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "Classifier", "step_name": "Classifier"}}]
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer),Classifier=sklearn.ensemble._forest.RandomForestClassifier)(4)_verbose	false
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(4)_n_jobs	null
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(4)_remainder	"drop"
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(4)_sparse_threshold	0.3
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(4)_transformer_weights	null
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(4)_transformers	[{"oml-python:serialized_object": "component_reference", "value": {"key": "categorical", "step_name": "categorical", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cat"}}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "continuous", "step_name": "continuous", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cont"}}}]
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(4)_verbose	false
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(4)_verbose_feature_names_out	true