Run
7051

Run 7051

Task 307 (Supervised Classification) kc2 Uploaded 02-03-2021 by Test Test
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  • openml-python Sklearn_0.24.1. study_937
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Flow

sklearn.ensemble._forest.RandomForestClassifier(1)A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is controlled with the `max_samples` parameter if `bootstrap=True` (default), otherwise the whole dataset is used to build each tree.
sklearn.ensemble._forest.RandomForestClassifier(1)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(1)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(1)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(1)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(1)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(1)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(1)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(1)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(1)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(1)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(1)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(1)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(1)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(1)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(1)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(1)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(1)_random_state6288
sklearn.ensemble._forest.RandomForestClassifier(1)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(1)_warm_startfalse

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.

18 Evaluation measures

Area under ROC curve
0.8079 ± 0.1018
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8272 ± 0.048
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4445 ± 0.1495
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.2432 ± 0.1285
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2195 ± 0.0299
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.3266 ± 0.0052
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8372 ± 0.0442
Cross-validation details (10-fold Crossvalidation)
Number of instances
522
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8244 ± 0.0514
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8372 ± 0.0442
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.7318 ± 0.0173
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.6722 ± 0.089
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4037 ± 0.0065
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3479 ± 0.043
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8617 ± 0.101
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.6999 ± 0.0747
Cross-validation details (10-fold Crossvalidation)