Run 10560537

Task 23 (Supervised Classification) cmc Uploaded 14-08-2021 by Sergey Redyuk
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sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer, OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,LogisticRegression=s klearn.linear_model.logistic.LogisticRegression)(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 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 to None.
sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,LogisticRegression=sklearn.linear_model.logistic.LogisticRegression)(2)_memory	null
sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,LogisticRegression=sklearn.linear_model.logistic.LogisticRegression)(2)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "Imputer", "step_name": "Imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "OneHotEncoder", "step_name": "OneHotEncoder"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "LogisticRegression", "step_name": "LogisticRegression"}}]
sklearn.preprocessing.imputation.Imputer(55)_axis	0
sklearn.preprocessing.imputation.Imputer(55)_copy	true
sklearn.preprocessing.imputation.Imputer(55)_missing_values	"NaN"
sklearn.preprocessing.imputation.Imputer(55)_strategy	"median"
sklearn.preprocessing.imputation.Imputer(55)_verbose	0
sklearn.preprocessing.data.OneHotEncoder(32)_categorical_features	"all"
sklearn.preprocessing.data.OneHotEncoder(32)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing.data.OneHotEncoder(32)_handle_unknown	"ignore"
sklearn.preprocessing.data.OneHotEncoder(32)_n_values	"auto"
sklearn.preprocessing.data.OneHotEncoder(32)_sparse	false
sklearn.linear_model.logistic.LogisticRegression(37)_C	1.0
sklearn.linear_model.logistic.LogisticRegression(37)_class_weight	null
sklearn.linear_model.logistic.LogisticRegression(37)_dual	false
sklearn.linear_model.logistic.LogisticRegression(37)_fit_intercept	true
sklearn.linear_model.logistic.LogisticRegression(37)_intercept_scaling	1
sklearn.linear_model.logistic.LogisticRegression(37)_max_iter	100
sklearn.linear_model.logistic.LogisticRegression(37)_multi_class	"ovr"
sklearn.linear_model.logistic.LogisticRegression(37)_n_jobs	1
sklearn.linear_model.logistic.LogisticRegression(37)_penalty	"l2"
sklearn.linear_model.logistic.LogisticRegression(37)_random_state	11870
sklearn.linear_model.logistic.LogisticRegression(37)_solver	"liblinear"
sklearn.linear_model.logistic.LogisticRegression(37)_tol	0.0001
sklearn.linear_model.logistic.LogisticRegression(37)_verbose	0
sklearn.linear_model.logistic.LogisticRegression(37)_warm_start	false