Run
5962713

Run 5962713

Task 3561 (Supervised Classification) profb Uploaded 15-07-2017 by Jan van Rijn
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  • openml-pimp openml-python Sklearn_0.18.1.
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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.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=skle arn.tree.tree.DecisionTreeClassifier))(1)Automatically created scikit-learn flow.
sklearn.tree.tree.DecisionTreeClassifier(10)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(10)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(10)_max_depth2
sklearn.tree.tree.DecisionTreeClassifier(10)_max_featuresnull
sklearn.tree.tree.DecisionTreeClassifier(10)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(10)_min_impurity_split1e-07
sklearn.tree.tree.DecisionTreeClassifier(10)_min_samples_leaf1
sklearn.tree.tree.DecisionTreeClassifier(10)_min_samples_split2
sklearn.tree.tree.DecisionTreeClassifier(10)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(10)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(10)_random_state35180
sklearn.tree.tree.DecisionTreeClassifier(10)_splitter"best"
openmlstudy14.preprocessing.ConditionalImputer(2)_axis0
openmlstudy14.preprocessing.ConditionalImputer(2)_categorical_features[3, 4, 7, 8]
openmlstudy14.preprocessing.ConditionalImputer(2)_copytrue
openmlstudy14.preprocessing.ConditionalImputer(2)_fill_empty0
openmlstudy14.preprocessing.ConditionalImputer(2)_missing_values"NaN"
openmlstudy14.preprocessing.ConditionalImputer(2)_strategy"median"
openmlstudy14.preprocessing.ConditionalImputer(2)_strategy_nominal"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(2)_verbose0
sklearn.preprocessing.data.OneHotEncoder(7)_categorical_features[3, 4, 7, 8]
sklearn.preprocessing.data.OneHotEncoder(7)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing.data.OneHotEncoder(7)_handle_unknown"ignore"
sklearn.preprocessing.data.OneHotEncoder(7)_n_values"auto"
sklearn.preprocessing.data.OneHotEncoder(7)_sparsefalse
sklearn.feature_selection.variance_threshold.VarianceThreshold(4)_threshold0.0
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(2)_algorithm"SAMME"
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(2)_learning_rate1.6854658078720979
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(2)_n_estimators139
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(2)_random_state11081

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
0.6897 ± 0.0543
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.6632 ± 0.0408
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.2241 ± 0.0998
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-135.511 ± 0.4742
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4969 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4446 ± 0.0022
Cross-validation details (10-fold Crossvalidation)
Number of instances
672
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.6595 ± 0.042
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.6771 ± 0.0289
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9188
Cross-validation details (10-fold Crossvalidation)
Recall
0.6771 ± 0.0289
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1.1176 ± 0.0051
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4714 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.497 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.0542 ± 0.0048
Cross-validation details (10-fold Crossvalidation)