Run 10454598

Task 3735 (Supervised Classification) pollen Uploaded 19-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_sele ction.GenericUnivariateSelect,step_1=sklearn.tree._classes.DecisionTreeClas sifier)(1)	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(3)_ccp_alpha	0.08062031840912076
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"gini"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	2
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.3718511976356482
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	1285
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.07842254573516177
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.46645840255673604
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.4213011830159717
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.006294900191684272
sklearn.tree._classes.DecisionTreeClassifier(3)_presort	"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state	42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter	"random"
sklearn.feature_selection._univariate_selection.GenericUnivariateSelect(1)_mode	"percentile"
sklearn.feature_selection._univariate_selection.GenericUnivariateSelect(1)_param	0.2511161168479791
sklearn.feature_selection._univariate_selection.GenericUnivariateSelect(1)_score_func	{"oml-python:serialized_object": "function", "value": "sklearn.feature_selection._univariate_selection.f_classif"}
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.GenericUnivariateSelect,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.GenericUnivariateSelect,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_1", "step_name": "step_1"}}]
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.GenericUnivariateSelect,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false