Run
8858249

Run 8858249

Task 146818 (Supervised Classification) Australian Uploaded 21-02-2018 by Jan van Rijn
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Flow

sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.Conditiona lImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethres hold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classif ier=sklearn.tree.tree.DecisionTreeClassifier)(4)Automatically created scikit-learn flow.
openmlstudy14.preprocessing.ConditionalImputer(6)_axis0
openmlstudy14.preprocessing.ConditionalImputer(6)_categorical_features[0, 3, 4, 5, 7, 8, 10, 11]
openmlstudy14.preprocessing.ConditionalImputer(6)_copytrue
openmlstudy14.preprocessing.ConditionalImputer(6)_fill_empty0
openmlstudy14.preprocessing.ConditionalImputer(6)_missing_values"NaN"
openmlstudy14.preprocessing.ConditionalImputer(6)_strategy"median"
openmlstudy14.preprocessing.ConditionalImputer(6)_strategy_nominal"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(6)_verbose0
sklearn.preprocessing.data.OneHotEncoder(18)_categorical_features[0, 3, 4, 5, 7, 8, 10, 11]
sklearn.preprocessing.data.OneHotEncoder(18)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing.data.OneHotEncoder(18)_handle_unknown"ignore"
sklearn.preprocessing.data.OneHotEncoder(18)_n_values"auto"
sklearn.preprocessing.data.OneHotEncoder(18)_sparsetrue
sklearn.feature_selection.variance_threshold.VarianceThreshold(12)_threshold0.0
sklearn.tree.tree.DecisionTreeClassifier(19)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(19)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(19)_max_depthnull
sklearn.tree.tree.DecisionTreeClassifier(19)_max_featuresnull
sklearn.tree.tree.DecisionTreeClassifier(19)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(19)_min_impurity_split1e-07
sklearn.tree.tree.DecisionTreeClassifier(19)_min_samples_leaf1
sklearn.tree.tree.DecisionTreeClassifier(19)_min_samples_split2
sklearn.tree.tree.DecisionTreeClassifier(19)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(19)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(19)_random_state33831
sklearn.tree.tree.DecisionTreeClassifier(19)_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
0.8154 ± 0.0413
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8149 ± 0.0436
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.6268 ± 0.0852
Cross-validation details (10-fold Crossvalidation)
KB relative information score
429.4526 ± 6.1018
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1855 ± 0.0436
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.494 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
Number of instances
690
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8167 ± 0.0403
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8145 ± 0.0436
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9913
Cross-validation details (10-fold Crossvalidation)
Recall
0.8145 ± 0.0436
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.3756 ± 0.0882
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.497 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4307 ± 0.0502
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8667 ± 0.1007
Cross-validation details (10-fold Crossvalidation)