Run
10595107

Run 10595107

Task 37 (Supervised Classification) diabetes Uploaded 07-07-2024 by Aatiz Ghimire
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Flow

sklearn.ensemble._forest.RandomForestClassifier(45)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(45)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(45)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(45)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(45)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(45)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(45)_max_features"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(45)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(45)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(45)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(45)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(45)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(45)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(45)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(45)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(45)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(45)_random_state57659
sklearn.ensemble._forest.RandomForestClassifier(45)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(45)_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
0.8262 ± 0.0438
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7689 ± 0.0462
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4844 ± 0.1043
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.3136 ± 0.0614
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.317 ± 0.023
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4545 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.7734 ± 0.0445
Cross-validation details (10-fold Crossvalidation)
Number of instances
768
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7686 ± 0.0479
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7734 ± 0.0445
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9331 ± 0.0032
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.6976 ± 0.0507
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
0.3995 ± 0.0255
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8383 ± 0.054
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.7342 ± 0.0515
Cross-validation details (10-fold Crossvalidation)