Run 2335

Task 11 (Supervised Classification) kr-vs-kp Uploaded 17-10-2024 by Continuous Integration
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads

Issue	#Downvotes for this reason	By

Flow

sklearn.pipeline.Pipeline(enc=sklearn.preprocessing._encoders.OneHotEncoder ,rs=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.en semble._forest.RandomForestClassifier))(5)	A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with a final :term:`predictor` for predictive modeling. Intermediate steps of the pipeline must be 'transforms', that is, they must implement `fit` and `transform` methods. The final :term:`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:`~s...
sklearn.preprocessing._encoders.OneHotEncoder(2)_categories	"auto"
sklearn.preprocessing._encoders.OneHotEncoder(2)_drop	null
sklearn.preprocessing._encoders.OneHotEncoder(2)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(2)_feature_name_combiner	"concat"
sklearn.preprocessing._encoders.OneHotEncoder(2)_handle_unknown	"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(2)_max_categories	null
sklearn.preprocessing._encoders.OneHotEncoder(2)_min_frequency	null
sklearn.preprocessing._encoders.OneHotEncoder(2)_sparse_output	true
sklearn.ensemble._forest.RandomForestClassifier(4)_bootstrap	true
sklearn.ensemble._forest.RandomForestClassifier(4)_ccp_alpha	0.0
sklearn.ensemble._forest.RandomForestClassifier(4)_class_weight	null
sklearn.ensemble._forest.RandomForestClassifier(4)_criterion	"gini"
sklearn.ensemble._forest.RandomForestClassifier(4)_max_depth	null
sklearn.ensemble._forest.RandomForestClassifier(4)_max_features	"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(4)_max_leaf_nodes	null
sklearn.ensemble._forest.RandomForestClassifier(4)_max_samples	null
sklearn.ensemble._forest.RandomForestClassifier(4)_min_impurity_decrease	0.0
sklearn.ensemble._forest.RandomForestClassifier(4)_min_samples_leaf	1
sklearn.ensemble._forest.RandomForestClassifier(4)_min_samples_split	2
sklearn.ensemble._forest.RandomForestClassifier(4)_min_weight_fraction_leaf	0.0
sklearn.ensemble._forest.RandomForestClassifier(4)_monotonic_cst	null
sklearn.ensemble._forest.RandomForestClassifier(4)_n_estimators	5
sklearn.ensemble._forest.RandomForestClassifier(4)_n_jobs	null
sklearn.ensemble._forest.RandomForestClassifier(4)_oob_score	false
sklearn.ensemble._forest.RandomForestClassifier(4)_random_state	33003
sklearn.ensemble._forest.RandomForestClassifier(4)_verbose	0
sklearn.ensemble._forest.RandomForestClassifier(4)_warm_start	false
sklearn.pipeline.Pipeline(enc=sklearn.preprocessing._encoders.OneHotEncoder,rs=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier))(5)_memory	null
sklearn.pipeline.Pipeline(enc=sklearn.preprocessing._encoders.OneHotEncoder,rs=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier))(5)_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))(5)_verbose	false
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(5)_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)(5)_error_score	NaN
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(5)_n_iter	2
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(5)_n_jobs	null
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(5)_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)(5)_pre_dispatch	"2*n_jobs"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(5)_random_state	12172
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(5)_refit	true
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(5)_return_train_score	false
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(5)_scoring	null
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(5)_verbose	0