Run
10709

Run 10709

Task 259 (Supervised Classification) collins Uploaded 29-11-2022 by Test Test
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  • openml-python Sklearn_1.1.2. study_2303
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Flow

sklearn.ensemble._forest.RandomForestClassifier(9)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(9)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(9)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(9)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(9)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(9)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(9)_max_features"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(9)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(9)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(9)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(9)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(9)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(9)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(9)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(9)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(9)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(9)_random_state33852
sklearn.ensemble._forest.RandomForestClassifier(9)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(9)_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
0.9941 ± 0.0027
Per class
F measure
0.8681 ± 0.0159
Per class
Kappa
0.865 ± 0.0628
KB relative information score
0.6604 ± 0.0202
Mean absolute error
0.0709 ± 0.0025
Mean prior absolute error
0.1212 ± 0.0002
Weighted recall
0.878 ± 0.0561
Number of instances
500
Per class
Precision
0.8825 ± 0.0247
Per class
Predictive accuracy
0.878 ± 0.0561
Prior entropy
3.6489 ± 0.0337
Relative absolute error
0.5851 ± 0.0207
Root mean prior squared error
0.246 ± 0.0003
Root mean squared error
0.1597 ± 0.0054
Root relative squared error
0.6492 ± 0.0221
Unweighted recall
0.768 ± 0.0421