Run
2012950

Run 2012950

Task 23 (Supervised Classification) cmc Uploaded 07-04-2017 by Jeroen van Hoof
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  • Fri_Apr__7_02.46.12_2017 NumPy_1.12.1. Python_3.5.3. run_task SciPy_0.19.0. sklearn.pipeline.Pipeline Sklearn_0.18.1.
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Flow

sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,oneho tencoder=sklearn.preprocessing.data.OneHotEncoder,randomforestclassifier=sk learn.ensemble.forest.RandomForestClassifier)(1)Automatically created scikit-learn flow.
sklearn.ensemble.forest.RandomForestClassifier(16)_bootstrapTrue
sklearn.ensemble.forest.RandomForestClassifier(16)_class_weightNone
sklearn.ensemble.forest.RandomForestClassifier(16)_criteriongini
sklearn.ensemble.forest.RandomForestClassifier(16)_max_depth9
sklearn.ensemble.forest.RandomForestClassifier(16)_max_featuresauto
sklearn.ensemble.forest.RandomForestClassifier(16)_max_leaf_nodesNone
sklearn.ensemble.forest.RandomForestClassifier(16)_min_impurity_split1e-07
sklearn.ensemble.forest.RandomForestClassifier(16)_min_samples_leaf30
sklearn.ensemble.forest.RandomForestClassifier(16)_min_samples_split17
sklearn.ensemble.forest.RandomForestClassifier(16)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(16)_n_estimators512
sklearn.ensemble.forest.RandomForestClassifier(16)_n_jobs1
sklearn.ensemble.forest.RandomForestClassifier(16)_oob_scoreFalse
sklearn.ensemble.forest.RandomForestClassifier(16)_random_state3
sklearn.ensemble.forest.RandomForestClassifier(16)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(16)_warm_startFalse
sklearn.preprocessing.data.OneHotEncoder(3)_categorical_features[False, True, True, False, True, True, True, True, True]
sklearn.preprocessing.data.OneHotEncoder(3)_dtype
sklearn.preprocessing.data.OneHotEncoder(3)_handle_unknownerror
sklearn.preprocessing.data.OneHotEncoder(3)_n_valuesauto
sklearn.preprocessing.data.OneHotEncoder(3)_sparseTrue
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)_steps[('dualimputer', ), ('onehotencoder', OneHotEncoder(categorical_features=[False, True, True, False, True, True, True, True, True], dtype=, handle_unknown='error', n_values='auto', sparse=True)), ('randomforestclassifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini', max_depth=9, max_features='auto', max_leaf_nodes=None, min_impurity_split=1e-07, min_samples_leaf=30, min_samples_split=17, min_weight_fraction_leaf=0.0, n_estimators=512, n_jobs=1, oob_score=False, random_state=3, verbose=0, warm_start=False))]

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
0.732
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.5147
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.249
Cross-validation details (10-fold Crossvalidation)
KB relative information score
216.7723
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3931
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4308
Cross-validation details (10-fold Crossvalidation)
Number of instances
1473
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.5181
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.5295
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.5392
Cross-validation details (10-fold Crossvalidation)
Recall
0.5295
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9125
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4641
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4344
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9359
Cross-validation details (10-fold Crossvalidation)