Run 10594026

Task 361901 (Supervised Classification) Weather Uploaded 07-08-2023 by said edsa
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sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transfo rmer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,n ominal=sklearn.preprocessing._encoders.OneHotEncoder),variancethreshold=skl earn.feature_selection._variance_threshold.VarianceThreshold,randomforestcl assifier=sklearn.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`.
sklearn.ensemble._forest.RandomForestClassifier(32)_bootstrap	true
sklearn.ensemble._forest.RandomForestClassifier(32)_ccp_alpha	0.0
sklearn.ensemble._forest.RandomForestClassifier(32)_class_weight	null
sklearn.ensemble._forest.RandomForestClassifier(32)_criterion	"gini"
sklearn.ensemble._forest.RandomForestClassifier(32)_max_depth	null
sklearn.ensemble._forest.RandomForestClassifier(32)_max_features	"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(32)_max_leaf_nodes	null
sklearn.ensemble._forest.RandomForestClassifier(32)_max_samples	null
sklearn.ensemble._forest.RandomForestClassifier(32)_min_impurity_decrease	0.0
sklearn.ensemble._forest.RandomForestClassifier(32)_min_samples_leaf	1
sklearn.ensemble._forest.RandomForestClassifier(32)_min_samples_split	2
sklearn.ensemble._forest.RandomForestClassifier(32)_min_weight_fraction_leaf	0.0
sklearn.ensemble._forest.RandomForestClassifier(32)_n_estimators	100
sklearn.ensemble._forest.RandomForestClassifier(32)_n_jobs	null
sklearn.ensemble._forest.RandomForestClassifier(32)_oob_score	false
sklearn.ensemble._forest.RandomForestClassifier(32)_random_state	55231
sklearn.ensemble._forest.RandomForestClassifier(32)_verbose	0
sklearn.ensemble._forest.RandomForestClassifier(32)_warm_start	false
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier)(1)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "variancethreshold", "step_name": "variancethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}]
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier)(1)_verbose	false
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_n_jobs	null
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_remainder	"passthrough"
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_sparse_threshold	0.3
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_transformer_weights	null
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_transformers	[{"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": [1, 2]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": [0, 3]}}]
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbose	false
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbose_feature_names_out	true
sklearn.preprocessing._data.StandardScaler(17)_copy	true
sklearn.preprocessing._data.StandardScaler(17)_with_mean	true
sklearn.preprocessing._data.StandardScaler(17)_with_std	true
sklearn.preprocessing._encoders.OneHotEncoder(48)_categories	"auto"
sklearn.preprocessing._encoders.OneHotEncoder(48)_drop	null
sklearn.preprocessing._encoders.OneHotEncoder(48)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(48)_handle_unknown	"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(48)_max_categories	null
sklearn.preprocessing._encoders.OneHotEncoder(48)_min_frequency	null
sklearn.preprocessing._encoders.OneHotEncoder(48)_sparse	"deprecated"
sklearn.preprocessing._encoders.OneHotEncoder(48)_sparse_output	true
sklearn.feature_selection._variance_threshold.VarianceThreshold(12)_threshold	0.0