Run
10160618

Run 10160618

Task 9 (Supervised Classification) autos Uploaded 04-04-2019 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_state30139
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
0.5759 ± 0.0671
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.3493
Per class
Kappa
0.1566 ± 0.1394
Cross-validation details (10-fold Crossvalidation)
KB relative information score
46.7181 ± 2.8324
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.177 ± 0.0422
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
0.4942
Per class
Predictive accuracy
0.3805 ± 0.1478
Cross-validation details (10-fold Crossvalidation)
Prior entropy
2.3268
Cross-validation details (10-fold Crossvalidation)
Recall
0.3805 ± 0.1478
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.8012 ± 0.1909
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
0.4207 ± 0.0483
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.2681 ± 0.1452
Cross-validation details (10-fold Crossvalidation)