Run
10593821

Run 10593821

Task 145677 (Supervised Classification) Bioresponse Uploaded 27-06-2023 by Luís Miguel Matos
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Flow

sklearn.ensemble._forest.RandomForestClassifier(28)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(28)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(28)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(28)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(28)_criterion"entropy"
sklearn.ensemble._forest.RandomForestClassifier(28)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(28)_max_features0.24812775010265953
sklearn.ensemble._forest.RandomForestClassifier(28)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(28)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(28)_min_impurity_decrease0.0015230626208161341
sklearn.ensemble._forest.RandomForestClassifier(28)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(28)_min_samples_split0.010701544745785316
sklearn.ensemble._forest.RandomForestClassifier(28)_min_weight_fraction_leaf0.0013724344071374753
sklearn.ensemble._forest.RandomForestClassifier(28)_n_estimators120
sklearn.ensemble._forest.RandomForestClassifier(28)_n_jobs-1
sklearn.ensemble._forest.RandomForestClassifier(28)_oob_scoretrue
sklearn.ensemble._forest.RandomForestClassifier(28)_random_state21317
sklearn.ensemble._forest.RandomForestClassifier(28)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(28)_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
0.8705 ± 0.019
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7995 ± 0.0197
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.5954 ± 0.0399
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.4069 ± 0.0245
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3125 ± 0.0111
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4964 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8001 ± 0.0197
Cross-validation details (10-fold Crossvalidation)
Number of instances
3751
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8 ± 0.0199
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8001 ± 0.0197
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9948 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.6296 ± 0.0223
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4982 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3821 ± 0.0131
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.7669 ± 0.0263
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.7964 ± 0.0199
Cross-validation details (10-fold Crossvalidation)