OpenML
10594061

Run 10594061

Task 10101 (Supervised Classification) blood-transfusion-service-center Uploaded 25-08-2023 by Anders Viklund
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Flow

sklearn.ensemble._forest.RandomForestClassifier(32)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(32)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(32)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(32)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(32)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(32)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(32)_max_features"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(32)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(32)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(32)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(32)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(32)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(32)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(32)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(32)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(32)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(32)_random_state1077
sklearn.ensemble._forest.RandomForestClassifier(32)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(32)_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
0.6899 ± 0.0555
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7235 ± 0.044
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.2005 ± 0.1306
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.0937 ± 0.0846
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3023 ± 0.0227
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.363 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.742 ± 0.0397
Cross-validation details (10-fold Crossvalidation)
Number of instances
748
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7143 ± 0.0528
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.742 ± 0.0397
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.7916 ± 0.0072
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.8327 ± 0.0619
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4258 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.427 ± 0.0268
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.0026 ± 0.0633
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5892 ± 0.063
Cross-validation details (10-fold Crossvalidation)