Run
10592432

Run 10592432

Task 11 (Supervised Classification) balance-scale Uploaded 23-03-2023 by Takeaki Sakabe
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=sklearn.ensemble._bagging.BaggingClassifier)(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(43)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(43)_copytrue
sklearn.impute._base.SimpleImputer(43)_fill_valuenull
sklearn.impute._base.SimpleImputer(43)_keep_empty_featuresfalse
sklearn.impute._base.SimpleImputer(43)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(43)_strategy"mean"
sklearn.impute._base.SimpleImputer(43)_verbose"deprecated"
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.ensemble._bagging.BaggingClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.ensemble._bagging.BaggingClassifier)(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=sklearn.ensemble._bagging.BaggingClassifier)(1)_verbosefalse
sklearn.ensemble._bagging.BaggingClassifier(1)_base_estimator"deprecated"
sklearn.ensemble._bagging.BaggingClassifier(1)_bootstraptrue
sklearn.ensemble._bagging.BaggingClassifier(1)_bootstrap_featuresfalse
sklearn.ensemble._bagging.BaggingClassifier(1)_estimatornull
sklearn.ensemble._bagging.BaggingClassifier(1)_max_features1.0
sklearn.ensemble._bagging.BaggingClassifier(1)_max_samples1.0
sklearn.ensemble._bagging.BaggingClassifier(1)_n_estimators10
sklearn.ensemble._bagging.BaggingClassifier(1)_n_jobsnull
sklearn.ensemble._bagging.BaggingClassifier(1)_oob_scorefalse
sklearn.ensemble._bagging.BaggingClassifier(1)_random_state13779
sklearn.ensemble._bagging.BaggingClassifier(1)_verbose0
sklearn.ensemble._bagging.BaggingClassifier(1)_warm_startfalse

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.

18 Evaluation measures

0.9337 ± 0.0125
Per class
Cross-validation details (10-fold Crossvalidation)
0.828 ± 0.0223
Per class
Cross-validation details (10-fold Crossvalidation)
0.6967 ± 0.0512
Cross-validation details (10-fold Crossvalidation)
0.5897 ± 0.0446
Cross-validation details (10-fold Crossvalidation)
0.1424 ± 0.0202
Cross-validation details (10-fold Crossvalidation)
0.3798 ± 0.0012
Cross-validation details (10-fold Crossvalidation)
0.8272 ± 0.0332
Cross-validation details (10-fold Crossvalidation)
625
Per class
Cross-validation details (10-fold Crossvalidation)
0.8291 ± 0.0205
Per class
Cross-validation details (10-fold Crossvalidation)
0.8272 ± 0.0332
Cross-validation details (10-fold Crossvalidation)
1.3181 ± 0.0124
Cross-validation details (10-fold Crossvalidation)
0.375 ± 0.0529
Cross-validation details (10-fold Crossvalidation)
0.4356 ± 0.0014
Cross-validation details (10-fold Crossvalidation)
0.2875 ± 0.0221
Cross-validation details (10-fold Crossvalidation)
0.66 ± 0.0499
Cross-validation details (10-fold Crossvalidation)
0.604 ± 0.0199
Cross-validation details (10-fold Crossvalidation)