Run 10594034

Task 361910 (Supervised Classification) Weather Uploaded 07-08-2023 by Chanita Panwoon
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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)(3)	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.preprocessing._encoders.OneHotEncoder(47)_categories	"auto"
sklearn.preprocessing._encoders.OneHotEncoder(47)_drop	null
sklearn.preprocessing._encoders.OneHotEncoder(47)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(47)_feature_name_combiner	"concat"
sklearn.preprocessing._encoders.OneHotEncoder(47)_handle_unknown	"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(47)_max_categories	null
sklearn.preprocessing._encoders.OneHotEncoder(47)_min_frequency	null
sklearn.preprocessing._encoders.OneHotEncoder(47)_sparse	"deprecated"
sklearn.preprocessing._encoders.OneHotEncoder(47)_sparse_output	true
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)(3)_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)(3)_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)(3)_verbose	false
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(3)_n_jobs	null
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(3)_remainder	"passthrough"
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(3)_sparse_threshold	0.3
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(3)_transformer_weights	null
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(3)_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)(3)_verbose	false
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(3)_verbose_feature_names_out	true
sklearn.preprocessing._data.StandardScaler(19)_copy	true
sklearn.preprocessing._data.StandardScaler(19)_with_mean	true
sklearn.preprocessing._data.StandardScaler(19)_with_std	true
sklearn.feature_selection._variance_threshold.VarianceThreshold(14)_threshold	0.0
sklearn.ensemble._forest.RandomForestClassifier(34)_bootstrap	true
sklearn.ensemble._forest.RandomForestClassifier(34)_ccp_alpha	0.0
sklearn.ensemble._forest.RandomForestClassifier(34)_class_weight	null
sklearn.ensemble._forest.RandomForestClassifier(34)_criterion	"gini"
sklearn.ensemble._forest.RandomForestClassifier(34)_max_depth	null
sklearn.ensemble._forest.RandomForestClassifier(34)_max_features	"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(34)_max_leaf_nodes	null
sklearn.ensemble._forest.RandomForestClassifier(34)_max_samples	null
sklearn.ensemble._forest.RandomForestClassifier(34)_min_impurity_decrease	0.0
sklearn.ensemble._forest.RandomForestClassifier(34)_min_samples_leaf	1
sklearn.ensemble._forest.RandomForestClassifier(34)_min_samples_split	2
sklearn.ensemble._forest.RandomForestClassifier(34)_min_weight_fraction_leaf	0.0
sklearn.ensemble._forest.RandomForestClassifier(34)_n_estimators	100
sklearn.ensemble._forest.RandomForestClassifier(34)_n_jobs	null
sklearn.ensemble._forest.RandomForestClassifier(34)_oob_score	false
sklearn.ensemble._forest.RandomForestClassifier(34)_random_state	51266
sklearn.ensemble._forest.RandomForestClassifier(34)_verbose	0
sklearn.ensemble._forest.RandomForestClassifier(34)_warm_start	false