Run
1460

Run 1460

Task 119 (Supervised Classification) diabetes Uploaded 25-02-2021 by Test Test
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Flow

sklearn.ensemble._forest.RandomForestClassifier(1)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(1)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(1)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(1)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(1)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(1)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(1)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(1)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(1)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(1)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(1)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(1)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(1)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(1)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(1)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(1)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(1)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(1)_random_state51389
sklearn.ensemble._forest.RandomForestClassifier(1)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(1)_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
0.8156
Per class
Cross-validation details (10% Holdout set)
F measure
0.7407
Per class
Cross-validation details (10% Holdout set)
Kappa
0.4357
Cross-validation details (10% Holdout set)
KB relative information score
0.3041
Cross-validation details (10% Holdout set)
Mean absolute error
0.3228
Cross-validation details (10% Holdout set)
Mean prior absolute error
0.4589
Cross-validation details (10% Holdout set)
Weighted recall
0.7431
Cross-validation details (10% Holdout set)
Number of instances
253
Per class
Cross-validation details (10% Holdout set)
Precision
0.7396
Per class
Cross-validation details (10% Holdout set)
Predictive accuracy
0.7431
Cross-validation details (10% Holdout set)
Prior entropy
0.9463
Cross-validation details (10% Holdout set)
Relative absolute error
0.7035
Cross-validation details (10% Holdout set)
Root mean prior squared error
0.4813
Cross-validation details (10% Holdout set)
Root mean squared error
0.4071
Cross-validation details (10% Holdout set)
Root relative squared error
0.846
Cross-validation details (10% Holdout set)
Unweighted recall
0.7143
Cross-validation details (10% Holdout set)