Run
8856

Run 8856

Task 307 (Supervised Classification) kc2 Uploaded 04-03-2021 by Test Test
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  • openml-python Sklearn_0.24.1. study_1165
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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_state13292
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

0.8051 ± 0.1066
Per class
Cross-validation details (10-fold Crossvalidation)
0.8337 ± 0.0512
Per class
Cross-validation details (10-fold Crossvalidation)
0.4662 ± 0.1582
Cross-validation details (10-fold Crossvalidation)
0.2494 ± 0.1403
Cross-validation details (10-fold Crossvalidation)
0.2188 ± 0.0337
Cross-validation details (10-fold Crossvalidation)
0.3266 ± 0.0052
Cross-validation details (10-fold Crossvalidation)
0.8429 ± 0.0484
Cross-validation details (10-fold Crossvalidation)
522
Per class
Cross-validation details (10-fold Crossvalidation)
0.8312 ± 0.0545
Per class
Cross-validation details (10-fold Crossvalidation)
0.8429 ± 0.0484
Cross-validation details (10-fold Crossvalidation)
0.7318 ± 0.0173
Cross-validation details (10-fold Crossvalidation)
0.67 ± 0.1005
Cross-validation details (10-fold Crossvalidation)
0.4037 ± 0.0065
Cross-validation details (10-fold Crossvalidation)
0.3468 ± 0.0459
Cross-validation details (10-fold Crossvalidation)
0.8591 ± 0.1082
Cross-validation details (10-fold Crossvalidation)
0.7105 ± 0.0744
Cross-validation details (10-fold Crossvalidation)