Run
10463499

Run 10463499

Task 3021 (Supervised Classification) sick Uploaded 21-05-2020 by Marc Zöller
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  • automl_meta_features openml-python Sklearn_0.22.1.
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Flow

sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step _1=sklearn.preprocessing._data.MinMaxScaler,step_2=sklearn.ensemble._weight _boosting.AdaBoostClassifier)(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.ensemble._weight_boosting.AdaBoostClassifier(2)_algorithm"SAMME.R"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_base_estimatornull
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_learning_rate1.1446588537302897e-06
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_n_estimators1415
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_random_state42
sklearn.preprocessing._data.MinMaxScaler(1)_copyfalse
sklearn.preprocessing._data.MinMaxScaler(1)_feature_range[0, 1]
sklearn.impute._base.MissingIndicator(1)_error_on_newtrue
sklearn.impute._base.MissingIndicator(1)_features"all"
sklearn.impute._base.MissingIndicator(1)_missing_valuesNaN
sklearn.impute._base.MissingIndicator(1)_sparse"auto"
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.MinMaxScaler,step_2=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.MinMaxScaler,step_2=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_1", "step_name": "step_1"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_2", "step_name": "step_2"}}]
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.MinMaxScaler,step_2=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_verbosefalse

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.

16 Evaluation measures

0.5931 ± 0.0154
Per class
Cross-validation details (10-fold Crossvalidation)
-0.5723 ± 0.0359
Cross-validation details (10-fold Crossvalidation)
0.1134 ± 0.0022
Cross-validation details (10-fold Crossvalidation)
0.1152 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
0.9388 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
3772
Per class
Cross-validation details (10-fold Crossvalidation)
0.9388 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
0.3324 ± 0.0031
Cross-validation details (10-fold Crossvalidation)
0.9845 ± 0.0163
Cross-validation details (10-fold Crossvalidation)
0.2398 ± 0.0014
Cross-validation details (10-fold Crossvalidation)
0.238 ± 0.0015
Cross-validation details (10-fold Crossvalidation)
0.9927 ± 0.0018
Cross-validation details (10-fold Crossvalidation)
0.5
Cross-validation details (10-fold Crossvalidation)