Run 10438129

Task 31 (Supervised Classification) credit-g Uploaded 02-04-2020 by Antonio Barata
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=xgboost.sklearn.XGBClassifier)(3)	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(14)_add_indicator	false
sklearn.impute._base.SimpleImputer(14)_copy	true
sklearn.impute._base.SimpleImputer(14)_fill_value	null
sklearn.impute._base.SimpleImputer(14)_missing_values	NaN
sklearn.impute._base.SimpleImputer(14)_strategy	"mean"
sklearn.impute._base.SimpleImputer(14)_verbose	0
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=xgboost.sklearn.XGBClassifier)(3)_memory	null
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=xgboost.sklearn.XGBClassifier)(3)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}]
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=xgboost.sklearn.XGBClassifier)(3)_verbose	false
xgboost.sklearn.XGBClassifier(10)_base_score	null
xgboost.sklearn.XGBClassifier(10)_booster	null
xgboost.sklearn.XGBClassifier(10)_colsample_bylevel	null
xgboost.sklearn.XGBClassifier(10)_colsample_bynode	null
xgboost.sklearn.XGBClassifier(10)_colsample_bytree	null
xgboost.sklearn.XGBClassifier(10)_gamma	null
xgboost.sklearn.XGBClassifier(10)_gpu_id	null
xgboost.sklearn.XGBClassifier(10)_importance_type	"gain"
xgboost.sklearn.XGBClassifier(10)_interaction_constraints	null
xgboost.sklearn.XGBClassifier(10)_learning_rate	null
xgboost.sklearn.XGBClassifier(10)_max_delta_step	null
xgboost.sklearn.XGBClassifier(10)_max_depth	null
xgboost.sklearn.XGBClassifier(10)_min_child_weight	null
xgboost.sklearn.XGBClassifier(10)_missing	NaN
xgboost.sklearn.XGBClassifier(10)_monotone_constraints	null
xgboost.sklearn.XGBClassifier(10)_n_estimators	100
xgboost.sklearn.XGBClassifier(10)_n_jobs	null
xgboost.sklearn.XGBClassifier(10)_num_parallel_tree	null
xgboost.sklearn.XGBClassifier(10)_objective	"binary:logistic"
xgboost.sklearn.XGBClassifier(10)_random_state	9687
xgboost.sklearn.XGBClassifier(10)_reg_alpha	null
xgboost.sklearn.XGBClassifier(10)_reg_lambda	null
xgboost.sklearn.XGBClassifier(10)_scale_pos_weight	null
xgboost.sklearn.XGBClassifier(10)_subsample	null
xgboost.sklearn.XGBClassifier(10)_tree_method	null
xgboost.sklearn.XGBClassifier(10)_validate_parameters	false
xgboost.sklearn.XGBClassifier(10)_verbosity	null