Run
1467

Run 1467

Task 307 (Supervised Classification) kc2 Uploaded 25-02-2021 by Test Test
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  • openml-python Sklearn_0.24.1. study_207
Issue #Downvotes for this reason By


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_state23592
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

0.7963 ± 0.1033
Per class
Cross-validation details (10-fold Crossvalidation)
0.8231 ± 0.0537
Per class
Cross-validation details (10-fold Crossvalidation)
0.4315 ± 0.162
Cross-validation details (10-fold Crossvalidation)
0.2299 ± 0.1447
Cross-validation details (10-fold Crossvalidation)
0.2252 ± 0.0335
Cross-validation details (10-fold Crossvalidation)
0.3266 ± 0.0052
Cross-validation details (10-fold Crossvalidation)
0.8333 ± 0.0534
Cross-validation details (10-fold Crossvalidation)
522
Per class
Cross-validation details (10-fold Crossvalidation)
0.8199 ± 0.0622
Per class
Cross-validation details (10-fold Crossvalidation)
0.8333 ± 0.0534
Cross-validation details (10-fold Crossvalidation)
0.7318 ± 0.0173
Cross-validation details (10-fold Crossvalidation)
0.6895 ± 0.0997
Cross-validation details (10-fold Crossvalidation)
0.4037 ± 0.0065
Cross-validation details (10-fold Crossvalidation)
0.3539 ± 0.0464
Cross-validation details (10-fold Crossvalidation)
0.8766 ± 0.1099
Cross-validation details (10-fold Crossvalidation)
0.694 ± 0.0708
Cross-validation details (10-fold Crossvalidation)