Run 7127

Task 6 (Supervised Classification) anneal Uploaded 18-11-2024 by Test Test
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sklearn.pipeline.Pipeline(transform=sklearn.compose._column_transformer.Col umnTransformer(cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preproce ssing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated _svd.TruncatedSVD),cont=sklearn.impute._base.SimpleImputer),estimator=sklea rn.ensemble._forest.RandomForestClassifier)(1)	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`. For an example use case of `Pipeline` combined with :class:`~sklearn.model_selection.GridSearchCV`, refer to :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py`. The example :ref:`sphx_glr_auto_exampl...
sklearn.ensemble._forest.RandomForestClassifier(1)_bootstrap	true
sklearn.ensemble._forest.RandomForestClassifier(1)_ccp_alpha	0.0
sklearn.ensemble._forest.RandomForestClassifier(1)_class_weight	null
sklearn.ensemble._forest.RandomForestClassifier(1)_criterion	"gini"
sklearn.ensemble._forest.RandomForestClassifier(1)_max_depth	10
sklearn.ensemble._forest.RandomForestClassifier(1)_max_features	"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(1)_max_leaf_nodes	null
sklearn.ensemble._forest.RandomForestClassifier(1)_max_samples	null
sklearn.ensemble._forest.RandomForestClassifier(1)_min_impurity_decrease	0.0
sklearn.ensemble._forest.RandomForestClassifier(1)_min_samples_leaf	1
sklearn.ensemble._forest.RandomForestClassifier(1)_min_samples_split	2
sklearn.ensemble._forest.RandomForestClassifier(1)_min_weight_fraction_leaf	0.0
sklearn.ensemble._forest.RandomForestClassifier(1)_n_estimators	50
sklearn.ensemble._forest.RandomForestClassifier(1)_n_jobs	null
sklearn.ensemble._forest.RandomForestClassifier(1)_oob_score	false
sklearn.ensemble._forest.RandomForestClassifier(1)_random_state	15001
sklearn.ensemble._forest.RandomForestClassifier(1)_verbose	0
sklearn.ensemble._forest.RandomForestClassifier(1)_warm_start	false
sklearn.preprocessing._encoders.OneHotEncoder(5)_categories	"auto"
sklearn.preprocessing._encoders.OneHotEncoder(5)_drop	null
sklearn.preprocessing._encoders.OneHotEncoder(5)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(5)_feature_name_combiner	"concat"
sklearn.preprocessing._encoders.OneHotEncoder(5)_handle_unknown	"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(5)_max_categories	null
sklearn.preprocessing._encoders.OneHotEncoder(5)_min_frequency	null
sklearn.preprocessing._encoders.OneHotEncoder(5)_sparse	"deprecated"
sklearn.preprocessing._encoders.OneHotEncoder(5)_sparse_output	true
sklearn.impute._base.SimpleImputer(1)_add_indicator	false
sklearn.impute._base.SimpleImputer(1)_copy	true
sklearn.impute._base.SimpleImputer(1)_fill_value	null
sklearn.impute._base.SimpleImputer(1)_keep_empty_features	false
sklearn.impute._base.SimpleImputer(1)_missing_values	NaN
sklearn.impute._base.SimpleImputer(1)_strategy	"median"
sklearn.pipeline.Pipeline(transform=sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=sklearn.impute._base.SimpleImputer),estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(transform=sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=sklearn.impute._base.SimpleImputer),estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "transform", "step_name": "transform"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}]
sklearn.pipeline.Pipeline(transform=sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=sklearn.impute._base.SimpleImputer),estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_verbose	false
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=sklearn.impute._base.SimpleImputer)(1)_n_jobs	null
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=sklearn.impute._base.SimpleImputer)(1)_remainder	"drop"
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=sklearn.impute._base.SimpleImputer)(1)_sparse_threshold	0.3
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=sklearn.impute._base.SimpleImputer)(1)_transformer_weights	null
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=sklearn.impute._base.SimpleImputer)(1)_transformers	[{"oml-python:serialized_object": "component_reference", "value": {"key": "cat", "step_name": "cat", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cat"}}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "cont", "step_name": "cont", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cont"}}}]
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=sklearn.impute._base.SimpleImputer)(1)_verbose	false
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=sklearn.impute._base.SimpleImputer)(1)_verbose_feature_names_out	true
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD)(1)_memory	null
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD)(1)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "truncatedsvd", "step_name": "truncatedsvd"}}]
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD)(1)_verbose	false
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_algorithm	"randomized"
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_n_components	2
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_n_iter	5
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_n_oversamples	10
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_power_iteration_normalizer	"auto"
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_random_state	60637
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_tol	0.0