Run 2286

Task 115 (Supervised Classification) diabetes Uploaded 17-10-2024 by Continuous Integration
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sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,Varian ceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold ,Estimator=sklearn.tree._classes.DecisionTreeClassifier)(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.tree._classes.DecisionTreeClassifier(5)_ccp_alpha	0.0
sklearn.tree._classes.DecisionTreeClassifier(5)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(5)_criterion	"gini"
sklearn.tree._classes.DecisionTreeClassifier(5)_max_depth	4
sklearn.tree._classes.DecisionTreeClassifier(5)_max_features	null
sklearn.tree._classes.DecisionTreeClassifier(5)_max_leaf_nodes	null
sklearn.tree._classes.DecisionTreeClassifier(5)_min_impurity_decrease	0.0
sklearn.tree._classes.DecisionTreeClassifier(5)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(5)_min_samples_leaf	1
sklearn.tree._classes.DecisionTreeClassifier(5)_min_samples_split	2
sklearn.tree._classes.DecisionTreeClassifier(5)_min_weight_fraction_leaf	0.0
sklearn.tree._classes.DecisionTreeClassifier(5)_presort	"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(5)_random_state	62501
sklearn.tree._classes.DecisionTreeClassifier(5)_splitter	"best"
sklearn.impute._base.SimpleImputer(5)_add_indicator	false
sklearn.impute._base.SimpleImputer(5)_copy	true
sklearn.impute._base.SimpleImputer(5)_fill_value	null
sklearn.impute._base.SimpleImputer(5)_missing_values	NaN
sklearn.impute._base.SimpleImputer(5)_strategy	"mean"
sklearn.impute._base.SimpleImputer(5)_verbose	0
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.tree._classes.DecisionTreeClassifier)(6)_memory	null
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.tree._classes.DecisionTreeClassifier)(6)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "Imputer", "step_name": "Imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "VarianceThreshold", "step_name": "VarianceThreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "Estimator", "step_name": "Estimator"}}]
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.tree._classes.DecisionTreeClassifier)(6)_verbose	false
sklearn.feature_selection._variance_threshold.VarianceThreshold(6)_threshold	0.05