Run
10587937

Run 10587937

Task 3954 (Supervised Classification) MagicTelescope Uploaded 12-09-2022 by Enol Fernández
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Flow

sklearn.ensemble._forest.RandomForestClassifier(11)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(11)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(11)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(11)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(11)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(11)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(11)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(11)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(11)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(11)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(11)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(11)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(11)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(11)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(11)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(11)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(11)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(11)_random_state56252
sklearn.ensemble._forest.RandomForestClassifier(11)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(11)_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

0.9368 ± 0.0059
Per class
Cross-validation details (10-fold Crossvalidation)
0.8791 ± 0.0054
Per class
Cross-validation details (10-fold Crossvalidation)
0.7317 ± 0.0123
Cross-validation details (10-fold Crossvalidation)
0.5978 ± 0.0085
Cross-validation details (10-fold Crossvalidation)
0.1941 ± 0.0037
Cross-validation details (10-fold Crossvalidation)
0.456 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.8811 ± 0.0052
Cross-validation details (10-fold Crossvalidation)
19020
Per class
Cross-validation details (10-fold Crossvalidation)
0.8809 ± 0.0052
Per class
Cross-validation details (10-fold Crossvalidation)
0.8811 ± 0.0052
Cross-validation details (10-fold Crossvalidation)
0.9355 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.4256 ± 0.008
Cross-validation details (10-fold Crossvalidation)
0.4775 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.2989 ± 0.0057
Cross-validation details (10-fold Crossvalidation)
0.6261 ± 0.0119
Cross-validation details (10-fold Crossvalidation)
0.8555 ± 0.0072
Cross-validation details (10-fold Crossvalidation)