Run 10459949

Task 3711 (Supervised Classification) elevators Uploaded 20-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.impute._knn.KNNImputer,step_1=skle arn.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	0.08993142507591756
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"entropy"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	14
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.6288793972762837
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	10829
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.10503559850211325
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.3850587446159341
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.07317982616612669
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.2457649020649887
sklearn.tree._classes.DecisionTreeClassifier(3)_presort	"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state	42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter	"random"
sklearn.pipeline.Pipeline(step_0=sklearn.impute._knn.KNNImputer,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.impute._knn.KNNImputer,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.impute._knn.KNNImputer,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false
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	36
sklearn.impute._knn.KNNImputer(1)_weights	"distance"