Run
10560714

Run 10560714

Task 37 (Supervised Classification) diabetes Uploaded 22-08-2021 by Usman Gohar
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Flow

sklearn.ensemble._forest.RandomForestClassifier(10)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(10)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(10)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(10)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(10)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(10)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(10)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(10)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(10)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(10)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(10)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(10)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(10)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(10)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(10)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(10)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(10)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(10)_random_state36232
sklearn.ensemble._forest.RandomForestClassifier(10)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(10)_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
0.8252 ± 0.0445
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.758 ± 0.0552
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4597 ± 0.1243
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.3094 ± 0.0655
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3186 ± 0.0243
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4545 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.763 ± 0.053
Cross-validation details (10-fold Crossvalidation)
Number of instances
768
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7575 ± 0.0564
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.763 ± 0.053
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9331 ± 0.0032
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.701 ± 0.0535
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
0.4004 ± 0.0268
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8401 ± 0.0564
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.7219 ± 0.0612
Cross-validation details (10-fold Crossvalidation)