Run
10160624

Run 10160624

Task 9 (Supervised Classification) autos Uploaded 05-04-2019 by Hugo Spee
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  • auto-jupyter-notebook openml-python Sklearn_0.20.1.
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute.SimpleImputer,estimator=sc ripts.machineLearningAlgorithms.WithoutOutliersClassifier(classifier=sklear n.discriminant_analysis.QuadraticDiscriminantAnalysis,outlierCLF=sklearn.en semble.iforest.IsolationForest))(1)Automatically created scikit-learn flow.
sklearn.impute.SimpleImputer(5)_copytrue
sklearn.impute.SimpleImputer(5)_fill_valuenull
sklearn.impute.SimpleImputer(5)_missing_valuesNaN
sklearn.impute.SimpleImputer(5)_strategy"mean"
sklearn.impute.SimpleImputer(5)_verbose0
sklearn.ensemble.iforest.IsolationForest(1)_behaviour"new"
sklearn.ensemble.iforest.IsolationForest(1)_bootstrapfalse
sklearn.ensemble.iforest.IsolationForest(1)_contamination"auto"
sklearn.ensemble.iforest.IsolationForest(1)_max_features1.0
sklearn.ensemble.iforest.IsolationForest(1)_max_samples"auto"
sklearn.ensemble.iforest.IsolationForest(1)_n_estimators100
sklearn.ensemble.iforest.IsolationForest(1)_n_jobsnull
sklearn.ensemble.iforest.IsolationForest(1)_random_state304
sklearn.ensemble.iforest.IsolationForest(1)_verbose0
sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis(1)_priorsnull
sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis(1)_reg_param0.0
sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis(1)_store_covariancefalse
sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis(1)_store_covariancesnull
sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis(1)_tol0.0001
sklearn.pipeline.Pipeline(imputer=sklearn.impute.SimpleImputer,estimator=scripts.machineLearningAlgorithms.WithoutOutliersClassifier(classifier=sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis,outlierCLF=sklearn.ensemble.iforest.IsolationForest))(1)_memorynull

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.

17 Evaluation measures

Area under ROC curve
0.5922 ± 0.0598
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.3519
Per class
Kappa
0.1853 ± 0.1258
Cross-validation details (10-fold Crossvalidation)
KB relative information score
50.5006 ± 2.8834
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.17 ± 0.0436
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2209 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
Number of instances
205
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.5563
Per class
Predictive accuracy
0.4049 ± 0.1525
Cross-validation details (10-fold Crossvalidation)
Prior entropy
2.3268
Cross-validation details (10-fold Crossvalidation)
Recall
0.4049 ± 0.1525
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7697 ± 0.1973
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3318 ± 0.0017
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4124 ± 0.0503
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.2429 ± 0.1517
Cross-validation details (10-fold Crossvalidation)