Run
10228700

Run 10228700

Task 31 (Supervised Classification) credit-g Uploaded 19-06-2019 by Tom Sterk
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer, estimator=sklearn.tree.tree.DecisionTreeClassifier)(18)Automatically created scikit-learn flow.
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)(18)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)(18)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}]
sklearn.preprocessing.imputation.Imputer(44)_axis0
sklearn.preprocessing.imputation.Imputer(44)_copytrue
sklearn.preprocessing.imputation.Imputer(44)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(44)_strategy"mean"
sklearn.preprocessing.imputation.Imputer(44)_verbose0
sklearn.tree.tree.DecisionTreeClassifier(44)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(44)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(44)_max_depthnull
sklearn.tree.tree.DecisionTreeClassifier(44)_max_featuresnull
sklearn.tree.tree.DecisionTreeClassifier(44)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(44)_min_impurity_decrease0.0
sklearn.tree.tree.DecisionTreeClassifier(44)_min_impurity_splitnull
sklearn.tree.tree.DecisionTreeClassifier(44)_min_samples_leaf1
sklearn.tree.tree.DecisionTreeClassifier(44)_min_samples_split2
sklearn.tree.tree.DecisionTreeClassifier(44)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(44)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(44)_random_state63549
sklearn.tree.tree.DecisionTreeClassifier(44)_splitter"best"

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
0.6431 ± 0.049
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.6886 ± 0.0377
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.2749 ± 0.0895
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.1905 ± 0.0978
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.317 ± 0.0383
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4202
Cross-validation details (10-fold Crossvalidation)
Number of instances
1000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.6966 ± 0.0392
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.683 ± 0.0383
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8813
Cross-validation details (10-fold Crossvalidation)
Recall
0.683 ± 0.0383
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7545 ± 0.0912
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4583
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.563 ± 0.0338
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.2286 ± 0.0738
Cross-validation details (10-fold Crossvalidation)