Run
10592473

Run 10592473

Task 15 (Supervised Classification) breast-w Uploaded 23-03-2023 by Takeaki Sakabe
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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_state37996
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

Area under ROC curve
0.984 ± 0.0097
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9528 ± 0.0153
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.8954 ± 0.0338
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.8606 ± 0.0321
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0652 ± 0.0138
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4519 ± 0.0014
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9528 ± 0.0152
Cross-validation details (10-fold Crossvalidation)
Number of instances
699
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9527 ± 0.0153
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9528 ± 0.0152
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9293 ± 0.0043
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.1443 ± 0.0305
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4753 ± 0.0015
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1859 ± 0.0291
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.3912 ± 0.061
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9473 ± 0.0213
Cross-validation details (10-fold Crossvalidation)