Run
6102013

Run 6102013

Task 4 (Supervised Classification) labor Uploaded 12-08-2017 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_depth5
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_state14258
sklearn.tree.tree.DecisionTreeClassifier(10)_splitter"best"
openmlstudy14.preprocessing.ConditionalImputer(2)_axis0
openmlstudy14.preprocessing.ConditionalImputer(2)_categorical_features[4, 6, 9, 11, 12, 13, 14, 15]
openmlstudy14.preprocessing.ConditionalImputer(2)_copytrue
openmlstudy14.preprocessing.ConditionalImputer(2)_fill_empty0
openmlstudy14.preprocessing.ConditionalImputer(2)_missing_values"NaN"
openmlstudy14.preprocessing.ConditionalImputer(2)_strategy"mean"
openmlstudy14.preprocessing.ConditionalImputer(2)_strategy_nominal"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(2)_verbose0
sklearn.preprocessing.data.OneHotEncoder(7)_categorical_features[4, 6, 9, 11, 12, 13, 14, 15]
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)_sparsetrue
sklearn.feature_selection.variance_threshold.VarianceThreshold(4)_threshold0.0
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(2)_algorithm"SAMME.R"
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(2)_learning_rate0.23941046653998796
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(2)_n_estimators397
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(2)_random_state31697

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
0.8459 ± 0.1748
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8596 ± 0.1214
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.6919 ± 0.3457
Cross-validation details (10-fold Crossvalidation)
KB relative information score
38.6377 ± 1.6781
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1404 ± 0.1328
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.457 ± 0.0093
Cross-validation details (10-fold Crossvalidation)
Number of instances
57
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8596 ± 0.1099
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8596 ± 0.1328
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9393
Cross-validation details (10-fold Crossvalidation)
Recall
0.8596 ± 0.1328
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.3071 ± 0.293
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4773 ± 0.0096
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3746 ± 0.2511
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.785 ± 0.5295
Cross-validation details (10-fold Crossvalidation)