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Task 11 (Supervised Classification) kr-vs-kp Uploaded 17-10-2024 by Continuous Integration
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sklearn.pipeline.Pipeline(enc=sklearn.preprocessing._encoders.OneHotEncoder ,rs=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.en semble._forest.RandomForestClassifier))(7)	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(8)_categories	"auto"
sklearn.preprocessing._encoders.OneHotEncoder(8)_drop	null
sklearn.preprocessing._encoders.OneHotEncoder(8)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(8)_handle_unknown	"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(8)_max_categories	null
sklearn.preprocessing._encoders.OneHotEncoder(8)_min_frequency	null
sklearn.preprocessing._encoders.OneHotEncoder(8)_sparse	"deprecated"
sklearn.preprocessing._encoders.OneHotEncoder(8)_sparse_output	true
sklearn.ensemble._forest.RandomForestClassifier(8)_bootstrap	true
sklearn.ensemble._forest.RandomForestClassifier(8)_ccp_alpha	0.0
sklearn.ensemble._forest.RandomForestClassifier(8)_class_weight	null
sklearn.ensemble._forest.RandomForestClassifier(8)_criterion	"gini"
sklearn.ensemble._forest.RandomForestClassifier(8)_max_depth	null
sklearn.ensemble._forest.RandomForestClassifier(8)_max_features	"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(8)_max_leaf_nodes	null
sklearn.ensemble._forest.RandomForestClassifier(8)_max_samples	null
sklearn.ensemble._forest.RandomForestClassifier(8)_min_impurity_decrease	0.0
sklearn.ensemble._forest.RandomForestClassifier(8)_min_samples_leaf	1
sklearn.ensemble._forest.RandomForestClassifier(8)_min_samples_split	2
sklearn.ensemble._forest.RandomForestClassifier(8)_min_weight_fraction_leaf	0.0
sklearn.ensemble._forest.RandomForestClassifier(8)_n_estimators	5
sklearn.ensemble._forest.RandomForestClassifier(8)_n_jobs	null
sklearn.ensemble._forest.RandomForestClassifier(8)_oob_score	false
sklearn.ensemble._forest.RandomForestClassifier(8)_random_state	33003
sklearn.ensemble._forest.RandomForestClassifier(8)_verbose	0
sklearn.ensemble._forest.RandomForestClassifier(8)_warm_start	false
sklearn.pipeline.Pipeline(enc=sklearn.preprocessing._encoders.OneHotEncoder,rs=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier))(7)_memory	null
sklearn.pipeline.Pipeline(enc=sklearn.preprocessing._encoders.OneHotEncoder,rs=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier))(7)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "enc", "step_name": "enc"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "rs", "step_name": "rs"}}]
sklearn.pipeline.Pipeline(enc=sklearn.preprocessing._encoders.OneHotEncoder,rs=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier))(7)_verbose	false
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(7)_cv	{"oml-python:serialized_object": "cv_object", "value": {"name": "sklearn.model_selection._split.StratifiedKFold", "parameters": {"n_splits": "2", "random_state": "62501", "shuffle": "true"}}}
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(7)_error_score	NaN
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(7)_n_iter	2
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(7)_n_jobs	null
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(7)_param_distributions	{"bootstrap": [true, false], "criterion": ["gini", "entropy"], "max_depth": [3, null], "max_features": [1, 2, 3, 4], "min_samples_leaf": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "min_samples_split": [2, 3, 4, 5, 6, 7, 8, 9, 10]}
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(7)_pre_dispatch	"2*n_jobs"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(7)_random_state	12172
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(7)_refit	true
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(7)_return_train_score	false
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(7)_scoring	null
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(7)_verbose	0