Run 10437813

Task 35 (Supervised Classification) dermatology Uploaded 03-03-2020 by Fares Gaaloul
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads

Issue	#Downvotes for this reason	By

Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=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.impute._base.SimpleImputer(11)_add_indicator	false
sklearn.impute._base.SimpleImputer(11)_copy	true
sklearn.impute._base.SimpleImputer(11)_fill_value	-1
sklearn.impute._base.SimpleImputer(11)_missing_values	NaN
sklearn.impute._base.SimpleImputer(11)_strategy	"constant"
sklearn.impute._base.SimpleImputer(11)_verbose	0
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=xgboost.sklearn.XGBClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=xgboost.sklearn.XGBClassifier)(1)_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)(1)_verbose	false
xgboost.sklearn.XGBClassifier(8)_base_score	0.5
xgboost.sklearn.XGBClassifier(8)_booster	"gbtree"
xgboost.sklearn.XGBClassifier(8)_colsample_bylevel	1
xgboost.sklearn.XGBClassifier(8)_colsample_bynode	1
xgboost.sklearn.XGBClassifier(8)_colsample_bytree	1
xgboost.sklearn.XGBClassifier(8)_gamma	0
xgboost.sklearn.XGBClassifier(8)_learning_rate	0.1
xgboost.sklearn.XGBClassifier(8)_max_delta_step	0
xgboost.sklearn.XGBClassifier(8)_max_depth	3
xgboost.sklearn.XGBClassifier(8)_min_child_weight	1
xgboost.sklearn.XGBClassifier(8)_missing	null
xgboost.sklearn.XGBClassifier(8)_n_estimators	100
xgboost.sklearn.XGBClassifier(8)_n_jobs	1
xgboost.sklearn.XGBClassifier(8)_nthread	null
xgboost.sklearn.XGBClassifier(8)_objective	"multi:softprob"
xgboost.sklearn.XGBClassifier(8)_random_state	42
xgboost.sklearn.XGBClassifier(8)_reg_alpha	0
xgboost.sklearn.XGBClassifier(8)_reg_lambda	1
xgboost.sklearn.XGBClassifier(8)_scale_pos_weight	1
xgboost.sklearn.XGBClassifier(8)_seed	null
xgboost.sklearn.XGBClassifier(8)_silent	null
xgboost.sklearn.XGBClassifier(8)_subsample	1
xgboost.sklearn.XGBClassifier(8)_verbosity	1