Run
10593819

Run 10593819

Task 37 (Supervised Classification) diabetes 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"log_loss"
sklearn.ensemble._forest.RandomForestClassifier(28)_max_depth56
sklearn.ensemble._forest.RandomForestClassifier(28)_max_features0.9576302483254024
sklearn.ensemble._forest.RandomForestClassifier(28)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(28)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(28)_min_impurity_decrease0.004305897848524732
sklearn.ensemble._forest.RandomForestClassifier(28)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(28)_min_samples_split0.0918412820301179
sklearn.ensemble._forest.RandomForestClassifier(28)_min_weight_fraction_leaf0.13153283093798368
sklearn.ensemble._forest.RandomForestClassifier(28)_n_estimators175
sklearn.ensemble._forest.RandomForestClassifier(28)_n_jobs-1
sklearn.ensemble._forest.RandomForestClassifier(28)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(28)_random_state63642
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
0.8238 ± 0.0471
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.756 ± 0.0591
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4524 ± 0.1335
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.2811 ± 0.0575
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3339 ± 0.0193
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4545 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.7643 ± 0.0568
Cross-validation details (10-fold Crossvalidation)
Number of instances
768
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7584 ± 0.0628
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7643 ± 0.0568
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9331 ± 0.0032
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7347 ± 0.0428
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4766 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4013 ± 0.0263
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.842 ± 0.0555
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.7143 ± 0.0663
Cross-validation details (10-fold Crossvalidation)