Run 10556117

Task 49 (Supervised Classification) tic-tac-toe Uploaded 08-08-2020 by Heinrich Peters
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sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer, onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,logisticregress ion=sklearn.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 it to 'passthrough' or ``None``.
sklearn.impute._base.SimpleImputer(11)_add_indicator	false
sklearn.impute._base.SimpleImputer(11)_copy	true
sklearn.impute._base.SimpleImputer(11)_fill_value	null
sklearn.impute._base.SimpleImputer(11)_missing_values	NaN
sklearn.impute._base.SimpleImputer(11)_strategy	"most_frequent"
sklearn.impute._base.SimpleImputer(11)_verbose	0
sklearn.preprocessing._encoders.OneHotEncoder(16)_categorical_features	null
sklearn.preprocessing._encoders.OneHotEncoder(16)_categories	null
sklearn.preprocessing._encoders.OneHotEncoder(16)_drop	null
sklearn.preprocessing._encoders.OneHotEncoder(16)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(16)_handle_unknown	"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(16)_n_values	null
sklearn.preprocessing._encoders.OneHotEncoder(16)_sparse	true
sklearn.linear_model.logistic.LogisticRegression(33)_C	0.001
sklearn.linear_model.logistic.LogisticRegression(33)_class_weight	null
sklearn.linear_model.logistic.LogisticRegression(33)_dual	false
sklearn.linear_model.logistic.LogisticRegression(33)_fit_intercept	true
sklearn.linear_model.logistic.LogisticRegression(33)_intercept_scaling	1
sklearn.linear_model.logistic.LogisticRegression(33)_l1_ratio	null
sklearn.linear_model.logistic.LogisticRegression(33)_max_iter	10000
sklearn.linear_model.logistic.LogisticRegression(33)_multi_class	"warn"
sklearn.linear_model.logistic.LogisticRegression(33)_n_jobs	null
sklearn.linear_model.logistic.LogisticRegression(33)_penalty	"l2"
sklearn.linear_model.logistic.LogisticRegression(33)_random_state	1
sklearn.linear_model.logistic.LogisticRegression(33)_solver	"lbfgs"
sklearn.linear_model.logistic.LogisticRegression(33)_tol	0.0001
sklearn.linear_model.logistic.LogisticRegression(33)_verbose	0
sklearn.linear_model.logistic.LogisticRegression(33)_warm_start	false
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,logisticregression=sklearn.linear_model.logistic.LogisticRegression)(2)_memory	null
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,logisticregression=sklearn.linear_model.logistic.LogisticRegression)(2)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"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.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,logisticregression=sklearn.linear_model.logistic.LogisticRegression)(2)_verbose	false