Run
10452848

Run 10452848

Task 37 (Supervised Classification) diabetes Uploaded 07-05-2020 by Kiady Mahefa Andry Manantena
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Flow

sklearn.ensemble.forest.RandomForestClassifier(59)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 always the same as the original input sample size but the samples are drawn with replacement if `bootstrap=True` (default).
sklearn.ensemble.forest.RandomForestClassifier(59)_bootstraptrue
sklearn.ensemble.forest.RandomForestClassifier(59)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(59)_criterion"gini"
sklearn.ensemble.forest.RandomForestClassifier(59)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(59)_max_features"auto"
sklearn.ensemble.forest.RandomForestClassifier(59)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(59)_min_impurity_decrease0.0
sklearn.ensemble.forest.RandomForestClassifier(59)_min_impurity_splitnull
sklearn.ensemble.forest.RandomForestClassifier(59)_min_samples_leaf1
sklearn.ensemble.forest.RandomForestClassifier(59)_min_samples_split2
sklearn.ensemble.forest.RandomForestClassifier(59)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(59)_n_estimators"warn"
sklearn.ensemble.forest.RandomForestClassifier(59)_n_jobsnull
sklearn.ensemble.forest.RandomForestClassifier(59)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(59)_random_state22853
sklearn.ensemble.forest.RandomForestClassifier(59)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(59)_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
0.7952 ± 0.0428
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7244 ± 0.0463
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.3811 ± 0.1043
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.2939 ± 0.0598
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3216 ± 0.0249
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4545 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.7344 ± 0.0419
Cross-validation details (10-fold Crossvalidation)
Number of instances
768
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7256 ± 0.0477
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7344 ± 0.0419
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9331 ± 0.0032
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7076 ± 0.0544
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
0.417 ± 0.0241
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8749 ± 0.0502
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.68 ± 0.0506
Cross-validation details (10-fold Crossvalidation)