Run 10560173

Task 9957 (Supervised Classification) qsar-biodeg Uploaded 13-08-2021 by Sergey Redyuk
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sklearn.pipeline.Pipeline(imputation=sklearn.preprocessing.imputation.Imput er,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=s klearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sk learn.tree.tree.DecisionTreeClassifier)(2)	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 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 to None.
sklearn.preprocessing.imputation.Imputer(52)_axis	0
sklearn.preprocessing.imputation.Imputer(52)_copy	true
sklearn.preprocessing.imputation.Imputer(52)_missing_values	"NaN"
sklearn.preprocessing.imputation.Imputer(52)_strategy	"median"
sklearn.preprocessing.imputation.Imputer(52)_verbose	0
sklearn.tree.tree.DecisionTreeClassifier(66)_class_weight	null
sklearn.tree.tree.DecisionTreeClassifier(66)_criterion	"entropy"
sklearn.tree.tree.DecisionTreeClassifier(66)_max_depth	0.9666063820857091
sklearn.tree.tree.DecisionTreeClassifier(66)_max_features	1.0
sklearn.tree.tree.DecisionTreeClassifier(66)_max_leaf_nodes	null
sklearn.tree.tree.DecisionTreeClassifier(66)_min_impurity_split	1e-07
sklearn.tree.tree.DecisionTreeClassifier(66)_min_samples_leaf	3
sklearn.tree.tree.DecisionTreeClassifier(66)_min_samples_split	9
sklearn.tree.tree.DecisionTreeClassifier(66)_min_weight_fraction_leaf	0.0
sklearn.tree.tree.DecisionTreeClassifier(66)_presort	false
sklearn.tree.tree.DecisionTreeClassifier(66)_random_state	29124
sklearn.tree.tree.DecisionTreeClassifier(66)_splitter	"best"
sklearn.pipeline.Pipeline(imputation=sklearn.preprocessing.imputation.Imputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.tree.tree.DecisionTreeClassifier)(2)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputation", "step_name": "imputation"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "hotencoding", "step_name": "hotencoding"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "variencethreshold", "step_name": "variencethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "classifier", "step_name": "classifier"}}]
sklearn.preprocessing.data.OneHotEncoder(30)_categorical_features	[3, 4, 7, 8]
sklearn.preprocessing.data.OneHotEncoder(30)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing.data.OneHotEncoder(30)_handle_unknown	"ignore"
sklearn.preprocessing.data.OneHotEncoder(30)_n_values	"auto"
sklearn.preprocessing.data.OneHotEncoder(30)_sparse	false
sklearn.feature_selection.variance_threshold.VarianceThreshold(30)_threshold	0.0