Run
5813690

Run 5813690

Task 2 (Supervised Classification) anneal Uploaded 13-07-2017 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.forest.RandomForestClassifier)(1)Automatically created scikit-learn flow.
sklearn.ensemble.forest.RandomForestClassifier(21)_bootstraptrue
sklearn.ensemble.forest.RandomForestClassifier(21)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(21)_criterion"entropy"
sklearn.ensemble.forest.RandomForestClassifier(21)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(21)_max_features0.14504586980392756
sklearn.ensemble.forest.RandomForestClassifier(21)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(21)_min_impurity_split1e-07
sklearn.ensemble.forest.RandomForestClassifier(21)_min_samples_leaf19
sklearn.ensemble.forest.RandomForestClassifier(21)_min_samples_split15
sklearn.ensemble.forest.RandomForestClassifier(21)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(21)_n_estimators100
sklearn.ensemble.forest.RandomForestClassifier(21)_n_jobs1
sklearn.ensemble.forest.RandomForestClassifier(21)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(21)_random_state41650
sklearn.ensemble.forest.RandomForestClassifier(21)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(21)_warm_startfalse
openmlstudy14.preprocessing.ConditionalImputer(2)_axis0
openmlstudy14.preprocessing.ConditionalImputer(2)_categorical_features[0, 1, 2, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 35, 36, 37]
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[0, 1, 2, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 35, 36, 37]
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

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.

15 Evaluation measures

Area under ROC curve
0.9327 ± 0.0298
Per class
Cross-validation details (10-fold Crossvalidation)
KB relative information score
351.0892 ± 3.6488
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.103 ± 0.0045
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1343 ± 0.0012
Cross-validation details (10-fold Crossvalidation)
Number of instances
898
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7617 ± 0.0054
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.2149
Cross-validation details (10-fold Crossvalidation)
Recall
0.7617 ± 0.0054
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7672 ± 0.0307
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2582 ± 0.0024
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2091 ± 0.0055
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.81 ± 0.0194
Cross-validation details (10-fold Crossvalidation)