Run 10459710

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	0.7810249694625817
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"gini"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	11
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.906847813430274
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	9868
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.054574997922188256
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.23821714969831778
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.36763602958156766
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.43304165917631326
sklearn.tree._classes.DecisionTreeClassifier(3)_presort	"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state	42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter	"best"
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	false
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	7
sklearn.impute._knn.KNNImputer(1)_weights	"distance"