Run 10456954

Task 3797 (Supervised Classification) socmob Uploaded 19-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.mu lti_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.im pute._knn.KNNImputer,step_2=sklearn.tree._classes.DecisionTreeClassifier)(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	0.8106322678170103
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"entropy"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	3
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.8420380670769164
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	231
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.0213669770886733
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.20962633196540054
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.32137857325329056
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.3928442145111607
sklearn.tree._classes.DecisionTreeClassifier(3)_presort	"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state	42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter	"best"
automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent(1)_columns	null
sklearn.impute._knn.KNNImputer(1)_add_indicator	true
sklearn.impute._knn.KNNImputer(1)_copy	false
sklearn.impute._knn.KNNImputer(1)_metric	"nan_euclidean"
sklearn.impute._knn.KNNImputer(1)_missing_values	NaN
sklearn.impute._knn.KNNImputer(1)_n_neighbors	13
sklearn.impute._knn.KNNImputer(1)_weights	"uniform"
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.impute._knn.KNNImputer,step_2=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.impute._knn.KNNImputer,step_2=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"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_2", "step_name": "step_2"}}]
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.impute._knn.KNNImputer,step_2=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false