Run
10456604

Run 10456604

Task 9914 (Supervised Classification) wilt Uploaded 19-05-2020 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=automl.components.data_preprocessing.imput ation.ImputationComponent,step_1=sklearn.ensemble._weight_boosting.AdaBoost Classifier)(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_rate1.8486117389021515
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_n_estimators31
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_random_state42
automl.components.data_preprocessing.imputation.ImputationComponent(1)_add_indicatortrue
automl.components.data_preprocessing.imputation.ImputationComponent(1)_missing_valuesNaN
automl.components.data_preprocessing.imputation.ImputationComponent(1)_strategy"median"
sklearn.pipeline.Pipeline(step_0=automl.components.data_preprocessing.imputation.ImputationComponent,step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.components.data_preprocessing.imputation.ImputationComponent,step_1=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"}}]
sklearn.pipeline.Pipeline(step_0=automl.components.data_preprocessing.imputation.ImputationComponent,step_1=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.

18 Evaluation measures

Area under ROC curve
0.971 ± 0.0157
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9758 ± 0.0046
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.7569 ± 0.0464
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-8.8292 ± 0.2567
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4424 ± 0.0181
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1022 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9764 ± 0.0046
Cross-validation details (10-fold Crossvalidation)
Number of instances
4839
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9755 ± 0.0047
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9764 ± 0.0046
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.3029 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
4.3282 ± 0.1754
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2259 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4449 ± 0.0148
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.9694 ± 0.0649
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.8593 ± 0.0302
Cross-validation details (10-fold Crossvalidation)