Run
10593881

Run 10593881

Task 3954 (Supervised Classification) MagicTelescope Uploaded 13-07-2023 by Stephen Thomas
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Flow

sklearn.ensemble._forest.RandomForestClassifier(31)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(31)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(31)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(31)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(31)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(31)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(31)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(31)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(31)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(31)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(31)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(31)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(31)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(31)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(31)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(31)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(31)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(31)_random_state63868
sklearn.ensemble._forest.RandomForestClassifier(31)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(31)_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

0.9365 ± 0.0052
Per class
Cross-validation details (10-fold Crossvalidation)
0.8811 ± 0.0056
Per class
Cross-validation details (10-fold Crossvalidation)
0.7361 ± 0.0129
Cross-validation details (10-fold Crossvalidation)
0.5978 ± 0.0083
Cross-validation details (10-fold Crossvalidation)
0.1941 ± 0.0035
Cross-validation details (10-fold Crossvalidation)
0.456 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.883 ± 0.0052
Cross-validation details (10-fold Crossvalidation)
19020
Per class
Cross-validation details (10-fold Crossvalidation)
0.8827 ± 0.005
Per class
Cross-validation details (10-fold Crossvalidation)
0.883 ± 0.0052
Cross-validation details (10-fold Crossvalidation)
0.9355 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.4256 ± 0.0076
Cross-validation details (10-fold Crossvalidation)
0.4775 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.2989 ± 0.0051
Cross-validation details (10-fold Crossvalidation)
0.6259 ± 0.0106
Cross-validation details (10-fold Crossvalidation)
0.858 ± 0.0081
Cross-validation details (10-fold Crossvalidation)