Run
10560803

Run 10560803

Task 3954 (Supervised Classification) MagicTelescope Uploaded 10-09-2021 by Joaquin Vanschoren
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  • openml-python Sklearn_0.22.2.post1.
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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 always the same as the original input sample size but the samples are drawn with replacement if `bootstrap=True` (default).
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"auto"
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_impurity_splitnull
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_state17933
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

0.9369 ± 0.006
Per class
Cross-validation details (10-fold Crossvalidation)
0.8799 ± 0.0058
Per class
Cross-validation details (10-fold Crossvalidation)
0.7335 ± 0.0131
Cross-validation details (10-fold Crossvalidation)
0.5972 ± 0.0093
Cross-validation details (10-fold Crossvalidation)
0.1943 ± 0.0037
Cross-validation details (10-fold Crossvalidation)
0.456 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.8818 ± 0.0056
Cross-validation details (10-fold Crossvalidation)
19020
Per class
Cross-validation details (10-fold Crossvalidation)
0.8814 ± 0.0057
Per class
Cross-validation details (10-fold Crossvalidation)
0.8818 ± 0.0056
Cross-validation details (10-fold Crossvalidation)
0.9355 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.4262 ± 0.008
Cross-validation details (10-fold Crossvalidation)
0.4775 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.2989 ± 0.006
Cross-validation details (10-fold Crossvalidation)
0.626 ± 0.0126
Cross-validation details (10-fold Crossvalidation)
0.8569 ± 0.0074
Cross-validation details (10-fold Crossvalidation)