Run 10462623

Task 3891 (Supervised Classification) gina_agnostic Uploaded 21-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=automl.components.data_preprocessing.imput ation.ImputationComponent,step_1=sklearn.tree._classes.DecisionTreeClassifi er)(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	0.010094128297424954
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"gini"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	491
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.2921412926875048
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	1439
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.045727689606567305
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.17343657134342572
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.39255694579086803
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.13665909671710436
sklearn.tree._classes.DecisionTreeClassifier(3)_presort	"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state	42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter	"random"
automl.components.data_preprocessing.imputation.ImputationComponent(1)_add_indicator	true
automl.components.data_preprocessing.imputation.ImputationComponent(1)_missing_values	NaN
automl.components.data_preprocessing.imputation.ImputationComponent(1)_strategy	"most_frequent"
sklearn.pipeline.Pipeline(step_0=automl.components.data_preprocessing.imputation.ImputationComponent,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=automl.components.data_preprocessing.imputation.ImputationComponent,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=automl.components.data_preprocessing.imputation.ImputationComponent,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false