Run
10160578

Run 10160578

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.tree.tree.DecisionTreeClassifier,outlierCLF=sklearn.ensemble.iforest.Isol ationForest))(1)Automatically created scikit-learn flow.
sklearn.tree.tree.DecisionTreeClassifier(32)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(32)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(32)_max_depthnull
sklearn.tree.tree.DecisionTreeClassifier(32)_max_featuresnull
sklearn.tree.tree.DecisionTreeClassifier(32)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(32)_min_impurity_decrease0.0
sklearn.tree.tree.DecisionTreeClassifier(32)_min_impurity_splitnull
sklearn.tree.tree.DecisionTreeClassifier(32)_min_samples_leaf1
sklearn.tree.tree.DecisionTreeClassifier(32)_min_samples_split2
sklearn.tree.tree.DecisionTreeClassifier(32)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(32)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(32)_random_state62542
sklearn.tree.tree.DecisionTreeClassifier(32)_splitter"best"
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.pipeline.Pipeline(imputer=sklearn.impute.SimpleImputer,estimator=scripts.machineLearningAlgorithms.WithoutOutliersClassifier(classifier=sklearn.tree.tree.DecisionTreeClassifier,outlierCLF=sklearn.ensemble.iforest.IsolationForest))(1)_memorynull
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_state11173
sklearn.ensemble.iforest.IsolationForest(1)_verbose0

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
0.8516 ± 0.0415
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7721 ± 0.0626
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.7039 ± 0.079
Cross-validation details (10-fold Crossvalidation)
KB relative information score
148.4862 ± 1.1953
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0655 ± 0.0178
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
0.7757 ± 0.0609
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7707 ± 0.0623
Cross-validation details (10-fold Crossvalidation)
Prior entropy
2.3268
Cross-validation details (10-fold Crossvalidation)
Recall
0.7707 ± 0.0623
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.2965 ± 0.081
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
0.2559 ± 0.0348
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.7715 ± 0.1058
Cross-validation details (10-fold Crossvalidation)