Run
10559872

Run 10559872

Task 6 (Supervised Classification) letter Uploaded 26-03-2021 by Tan Zheng
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  • openml-python Sklearn_0.23.2. study_273
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Flow

sklearn.ensemble._forest.RandomForestClassifier(6)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(6)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(6)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(6)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(6)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(6)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(6)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(6)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(6)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(6)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(6)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(6)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(6)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(6)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(6)_n_estimators10
sklearn.ensemble._forest.RandomForestClassifier(6)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(6)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(6)_random_state12612
sklearn.ensemble._forest.RandomForestClassifier(6)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(6)_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

0.9956 ± 0.0008
Per class
Cross-validation details (10-fold Crossvalidation)
0.9421 ± 0.0038
Per class
Cross-validation details (10-fold Crossvalidation)
0.9396 ± 0.0039
Cross-validation details (10-fold Crossvalidation)
0.9142 ± 0.0016
Cross-validation details (10-fold Crossvalidation)
0.0136 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.074 ± 0
Cross-validation details (10-fold Crossvalidation)
0.9419 ± 0.0038
Cross-validation details (10-fold Crossvalidation)
20000
Per class
Cross-validation details (10-fold Crossvalidation)
0.9429 ± 0.0038
Per class
Cross-validation details (10-fold Crossvalidation)
0.9419 ± 0.0038
Cross-validation details (10-fold Crossvalidation)
4.6998 ± 0
Cross-validation details (10-fold Crossvalidation)
0.1845 ± 0.0038
Cross-validation details (10-fold Crossvalidation)
0.1923 ± 0
Cross-validation details (10-fold Crossvalidation)
0.0697 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
0.3625 ± 0.004
Cross-validation details (10-fold Crossvalidation)
0.9416 ± 0.0038
Cross-validation details (10-fold Crossvalidation)