Run 10559763

Task 10093 (Supervised Classification) banknote-authentication Uploaded 23-02-2021 by Fabrice Normandin
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sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.Standar dScaler,xgbclassifier=xgboost.sklearn.XGBClassifier)(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.preprocessing.data.StandardScaler(41)_copy	true
sklearn.preprocessing.data.StandardScaler(41)_with_mean	true
sklearn.preprocessing.data.StandardScaler(41)_with_std	true
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,xgbclassifier=xgboost.sklearn.XGBClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,xgbclassifier=xgboost.sklearn.XGBClassifier)(1)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "xgbclassifier", "step_name": "xgbclassifier"}}]
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,xgbclassifier=xgboost.sklearn.XGBClassifier)(1)_verbose	false
xgboost.sklearn.XGBClassifier(11)_base_score	null
xgboost.sklearn.XGBClassifier(11)_booster	"gblinear"
xgboost.sklearn.XGBClassifier(11)_colsample_bylevel	0.10236229212682288
xgboost.sklearn.XGBClassifier(11)_colsample_bynode	null
xgboost.sklearn.XGBClassifier(11)_colsample_bytree	0.9133756966924026
xgboost.sklearn.XGBClassifier(11)_gamma	null
xgboost.sklearn.XGBClassifier(11)_gpu_id	null
xgboost.sklearn.XGBClassifier(11)_importance_type	"gain"
xgboost.sklearn.XGBClassifier(11)_interaction_constraints	null
xgboost.sklearn.XGBClassifier(11)_learning_rate	0.7046906880432579
xgboost.sklearn.XGBClassifier(11)_max_delta_step	null
xgboost.sklearn.XGBClassifier(11)_max_depth	14
xgboost.sklearn.XGBClassifier(11)_min_child_weight	93.9943569048485
xgboost.sklearn.XGBClassifier(11)_missing	NaN
xgboost.sklearn.XGBClassifier(11)_monotone_constraints	null
xgboost.sklearn.XGBClassifier(11)_n_estimators	100
xgboost.sklearn.XGBClassifier(11)_n_jobs	null
xgboost.sklearn.XGBClassifier(11)_num_parallel_tree	null
xgboost.sklearn.XGBClassifier(11)_objective	"binary:logistic"
xgboost.sklearn.XGBClassifier(11)_random_state	33860
xgboost.sklearn.XGBClassifier(11)_reg_alpha	827.8481363932908
xgboost.sklearn.XGBClassifier(11)_reg_lambda	769.6869249686737
xgboost.sklearn.XGBClassifier(11)_scale_pos_weight	null
xgboost.sklearn.XGBClassifier(11)_subsample	0.8189293035021832
xgboost.sklearn.XGBClassifier(11)_tree_method	null
xgboost.sklearn.XGBClassifier(11)_use_label_encoder	true
xgboost.sklearn.XGBClassifier(11)_validate_parameters	null
xgboost.sklearn.XGBClassifier(11)_verbosity	null