Run
10559740

Run 10559740

Task 23 (Supervised Classification) cmc Uploaded 30-10-2020 by Heinrich Peters
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


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_estimators100
sklearn.ensemble._forest.RandomForestClassifier(6)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(6)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(6)_random_state50940
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

Area under ROC curve
0.7032 ± 0.0393
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.5144 ± 0.0391
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.2469 ± 0.0617
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.2498 ± 0.0369
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3526 ± 0.0145
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4308 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.518 ± 0.0395
Cross-validation details (10-fold Crossvalidation)
Number of instances
1473
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.5124 ± 0.0404
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.518 ± 0.0395
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.539 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.8185 ± 0.0336
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4641 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4472 ± 0.0172
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9635 ± 0.037
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.4891 ± 0.0364
Cross-validation details (10-fold Crossvalidation)