Run
10437846

Run 10437846

Task 3491 (Supervised Classification) hayes-roth Uploaded 03-03-2020 by Fares Gaaloul
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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_indicatorfalse
sklearn.impute._base.SimpleImputer(11)_copytrue
sklearn.impute._base.SimpleImputer(11)_fill_value-1
sklearn.impute._base.SimpleImputer(11)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(11)_strategy"constant"
sklearn.impute._base.SimpleImputer(11)_verbose0
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=xgboost.sklearn.XGBClassifier)(1)_memorynull
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)_verbosefalse
xgboost.sklearn.XGBClassifier(8)_base_score0.5
xgboost.sklearn.XGBClassifier(8)_booster"gbtree"
xgboost.sklearn.XGBClassifier(8)_colsample_bylevel1
xgboost.sklearn.XGBClassifier(8)_colsample_bynode1
xgboost.sklearn.XGBClassifier(8)_colsample_bytree1
xgboost.sklearn.XGBClassifier(8)_gamma0
xgboost.sklearn.XGBClassifier(8)_learning_rate0.1
xgboost.sklearn.XGBClassifier(8)_max_delta_step0
xgboost.sklearn.XGBClassifier(8)_max_depth3
xgboost.sklearn.XGBClassifier(8)_min_child_weight1
xgboost.sklearn.XGBClassifier(8)_missingnull
xgboost.sklearn.XGBClassifier(8)_n_estimators100
xgboost.sklearn.XGBClassifier(8)_n_jobs1
xgboost.sklearn.XGBClassifier(8)_nthreadnull
xgboost.sklearn.XGBClassifier(8)_objective"multi:softprob"
xgboost.sklearn.XGBClassifier(8)_random_state42
xgboost.sklearn.XGBClassifier(8)_reg_alpha0
xgboost.sklearn.XGBClassifier(8)_reg_lambda1
xgboost.sklearn.XGBClassifier(8)_scale_pos_weight1
xgboost.sklearn.XGBClassifier(8)_seednull
xgboost.sklearn.XGBClassifier(8)_silentnull
xgboost.sklearn.XGBClassifier(8)_subsample1
xgboost.sklearn.XGBClassifier(8)_verbosity1

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

17 Evaluation measures

0.9527 ± 0.0458
Per class
Cross-validation details (10-fold Crossvalidation)
0.7892 ± 0.1218
Per class
Cross-validation details (10-fold Crossvalidation)
0.6646 ± 0.1939
Cross-validation details (10-fold Crossvalidation)
0.7294 ± 0.1047
Cross-validation details (10-fold Crossvalidation)
0.1033 ± 0.0367
Cross-validation details (10-fold Crossvalidation)
0.3201 ± 0.002
Cross-validation details (10-fold Crossvalidation)
0.7875 ± 0.1186
Cross-validation details (10-fold Crossvalidation)
160
Per class
Cross-validation details (10-fold Crossvalidation)
0.7923 ± 0.1095
Per class
Cross-validation details (10-fold Crossvalidation)
0.7875 ± 0.1186
Cross-validation details (10-fold Crossvalidation)
1.5243 ± 0.0205
Cross-validation details (10-fold Crossvalidation)
0.3228 ± 0.1153
Cross-validation details (10-fold Crossvalidation)
0.3992 ± 0.0025
Cross-validation details (10-fold Crossvalidation)
0.224 ± 0.0646
Cross-validation details (10-fold Crossvalidation)
0.5612 ± 0.1627
Cross-validation details (10-fold Crossvalidation)