Run
3522

Run 3522

Task 119 (Supervised Classification) diabetes Uploaded 01-03-2021 by Test Test
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Flow

sklearn.ensemble._forest.RandomForestClassifier(2)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(2)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(2)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(2)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(2)_random_state34084
sklearn.ensemble._forest.RandomForestClassifier(2)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(2)_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
0.8191
Per class
Cross-validation details (10% Holdout set)
F measure
0.7501
Per class
Cross-validation details (10% Holdout set)
Kappa
0.4582
Cross-validation details (10% Holdout set)
KB relative information score
0.3082
Cross-validation details (10% Holdout set)
Mean absolute error
0.322
Cross-validation details (10% Holdout set)
Mean prior absolute error
0.4589
Cross-validation details (10% Holdout set)
Weighted recall
0.751
Cross-validation details (10% Holdout set)
Number of instances
253
Per class
Cross-validation details (10% Holdout set)
Precision
0.7494
Per class
Cross-validation details (10% Holdout set)
Predictive accuracy
0.751
Cross-validation details (10% Holdout set)
Prior entropy
0.9463
Cross-validation details (10% Holdout set)
Relative absolute error
0.7017
Cross-validation details (10% Holdout set)
Root mean prior squared error
0.4813
Cross-validation details (10% Holdout set)
Root mean squared error
0.4061
Cross-validation details (10% Holdout set)
Root relative squared error
0.8438
Cross-validation details (10% Holdout set)
Unweighted recall
0.7275
Cross-validation details (10% Holdout set)