Run
3964

Run 3964

Task 307 (Supervised Classification) kc2 Uploaded 01-03-2021 by Test Test
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
  • openml-python Sklearn_0.23.1. study_530
Issue #Downvotes for this reason By


Flow

sklearn.ensemble._forest.RandomForestClassifier(2)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(2)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(2)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(2)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(2)_random_state56792
sklearn.ensemble._forest.RandomForestClassifier(2)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(2)_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

Area under ROC curve
0.8089 ± 0.099
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8215 ± 0.0489
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4271 ± 0.1458
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.2419 ± 0.1449
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2205 ± 0.0344
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.3266 ± 0.0052
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8314 ± 0.0494
Cross-validation details (10-fold Crossvalidation)
Number of instances
522
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8181 ± 0.056
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8314 ± 0.0494
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.7318 ± 0.0173
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.6751 ± 0.1025
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4037 ± 0.0065
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3487 ± 0.0472
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8639 ± 0.1116
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.6928 ± 0.0598
Cross-validation details (10-fold Crossvalidation)