Run
10594598

Run 10594598

Task 15 (Supervised Classification) breast-w Uploaded 12-04-2024 by Gonçalo Esteves
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  • openml-python Sklearn_1.4.1.post1.
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(3)A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with a final :term:`predictor` for predictive modeling. Intermediate steps of the pipeline must be 'transforms', that is, they must implement `fit` and `transform` methods. The final :term:`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`. For an example use case of `Pipeline` combined with :class:`~s...
sklearn.impute._base.SimpleImputer(55)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(55)_copytrue
sklearn.impute._base.SimpleImputer(55)_fill_valuenull
sklearn.impute._base.SimpleImputer(55)_keep_empty_featuresfalse
sklearn.impute._base.SimpleImputer(55)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(55)_strategy"mean"
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(3)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(3)_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._weight_boosting.AdaBoostClassifier)(3)_verbosefalse
sklearn.ensemble._weight_boosting.AdaBoostClassifier(8)_algorithm"SAMME.R"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(8)_estimatornull
sklearn.ensemble._weight_boosting.AdaBoostClassifier(8)_learning_rate1.0
sklearn.ensemble._weight_boosting.AdaBoostClassifier(8)_n_estimators50
sklearn.ensemble._weight_boosting.AdaBoostClassifier(8)_random_state48148

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

0.9834 ± 0.0079
Per class
Cross-validation details (10-fold Crossvalidation)
0.9613 ± 0.0179
Per class
Cross-validation details (10-fold Crossvalidation)
0.9143 ± 0.0393
Cross-validation details (10-fold Crossvalidation)
0.164 ± 0.1124
Cross-validation details (10-fold Crossvalidation)
0.3777 ± 0.0406
Cross-validation details (10-fold Crossvalidation)
0.4519 ± 0.0014
Cross-validation details (10-fold Crossvalidation)
0.9614 ± 0.0179
Cross-validation details (10-fold Crossvalidation)
699
Per class
Cross-validation details (10-fold Crossvalidation)
0.9613 ± 0.0179
Per class
Cross-validation details (10-fold Crossvalidation)
0.9614 ± 0.0179
Cross-validation details (10-fold Crossvalidation)
0.9293 ± 0.0043
Cross-validation details (10-fold Crossvalidation)
0.8357 ± 0.0894
Cross-validation details (10-fold Crossvalidation)
0.4753 ± 0.0015
Cross-validation details (10-fold Crossvalidation)
0.3967 ± 0.0345
Cross-validation details (10-fold Crossvalidation)
0.8347 ± 0.0722
Cross-validation details (10-fold Crossvalidation)
0.9558 ± 0.019
Cross-validation details (10-fold Crossvalidation)