Run
10595141

Run 10595141

Task 37 (Supervised Classification) diabetes Uploaded 01-09-2024 by Kamal Sapkota
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Flow

sklearn.ensemble._forest.RandomForestClassifier(43)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. For a comparison between tree-based ensemble models see the example :ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py`.
sklearn.ensemble._forest.RandomForestClassifier(43)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(43)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(43)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(43)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(43)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(43)_max_features"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(43)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(43)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(43)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(43)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(43)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(43)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(43)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(43)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(43)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(43)_random_state26162
sklearn.ensemble._forest.RandomForestClassifier(43)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(43)_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
0.8278 ± 0.0391
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7687 ± 0.0408
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4835 ± 0.0913
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.3134 ± 0.0569
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3175 ± 0.0211
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4545 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.7734 ± 0.0393
Cross-validation details (10-fold Crossvalidation)
Number of instances
768
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7685 ± 0.0423
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7734 ± 0.0393
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9331 ± 0.0032
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.6986 ± 0.0466
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
0.3988 ± 0.0237
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8366 ± 0.0504
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.7334 ± 0.0441
Cross-validation details (10-fold Crossvalidation)