Run 6581

Task 11 (Supervised Classification) kr-vs-kp Uploaded 12-11-2024 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))(6)	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(6)_categories	"auto"
sklearn.preprocessing._encoders.OneHotEncoder(6)_drop	null
sklearn.preprocessing._encoders.OneHotEncoder(6)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(6)_handle_unknown	"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(6)_sparse	true
sklearn.ensemble._forest.RandomForestClassifier(6)_bootstrap	true
sklearn.ensemble._forest.RandomForestClassifier(6)_ccp_alpha	0.0
sklearn.ensemble._forest.RandomForestClassifier(6)_class_weight	null
sklearn.ensemble._forest.RandomForestClassifier(6)_criterion	"gini"
sklearn.ensemble._forest.RandomForestClassifier(6)_max_depth	null
sklearn.ensemble._forest.RandomForestClassifier(6)_max_features	"auto"
sklearn.ensemble._forest.RandomForestClassifier(6)_max_leaf_nodes	null
sklearn.ensemble._forest.RandomForestClassifier(6)_max_samples	null
sklearn.ensemble._forest.RandomForestClassifier(6)_min_impurity_decrease	0.0
sklearn.ensemble._forest.RandomForestClassifier(6)_min_impurity_split	null
sklearn.ensemble._forest.RandomForestClassifier(6)_min_samples_leaf	1
sklearn.ensemble._forest.RandomForestClassifier(6)_min_samples_split	2
sklearn.ensemble._forest.RandomForestClassifier(6)_min_weight_fraction_leaf	0.0
sklearn.ensemble._forest.RandomForestClassifier(6)_n_estimators	5
sklearn.ensemble._forest.RandomForestClassifier(6)_n_jobs	null
sklearn.ensemble._forest.RandomForestClassifier(6)_oob_score	false
sklearn.ensemble._forest.RandomForestClassifier(6)_random_state	33003
sklearn.ensemble._forest.RandomForestClassifier(6)_verbose	0
sklearn.ensemble._forest.RandomForestClassifier(6)_warm_start	false
sklearn.pipeline.Pipeline(enc=sklearn.preprocessing._encoders.OneHotEncoder,rs=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier))(6)_memory	null
sklearn.pipeline.Pipeline(enc=sklearn.preprocessing._encoders.OneHotEncoder,rs=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier))(6)_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))(6)_verbose	false
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(6)_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)(6)_error_score	NaN
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(6)_iid	"deprecated"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(6)_n_iter	2
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(6)_n_jobs	null
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(6)_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)(6)_pre_dispatch	"2*n_jobs"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(6)_random_state	12172
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(6)_refit	true
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(6)_return_train_score	false
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(6)_scoring	null
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(6)_verbose	0