Run
10706

Run 10706

Task 119 (Supervised Classification) diabetes Uploaded 07-11-2019 by Test Test
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Flow

sklearn.ensemble.forest.RandomForestClassifier(16)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(16)_bootstraptrue
sklearn.ensemble.forest.RandomForestClassifier(16)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(16)_criterion"gini"
sklearn.ensemble.forest.RandomForestClassifier(16)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(16)_max_features"auto"
sklearn.ensemble.forest.RandomForestClassifier(16)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(16)_min_impurity_decrease0.0
sklearn.ensemble.forest.RandomForestClassifier(16)_min_impurity_splitnull
sklearn.ensemble.forest.RandomForestClassifier(16)_min_samples_leaf1
sklearn.ensemble.forest.RandomForestClassifier(16)_min_samples_split2
sklearn.ensemble.forest.RandomForestClassifier(16)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(16)_n_estimators"warn"
sklearn.ensemble.forest.RandomForestClassifier(16)_n_jobsnull
sklearn.ensemble.forest.RandomForestClassifier(16)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(16)_random_state8565
sklearn.ensemble.forest.RandomForestClassifier(16)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(16)_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
0.7729
Per class
Cross-validation details (10% Holdout set)
F measure
0.6944
Per class
Cross-validation details (10% Holdout set)
Kappa
0.3273
Cross-validation details (10% Holdout set)
KB relative information score
0.2808
Cross-validation details (10% Holdout set)
Mean absolute error
0.3289
Cross-validation details (10% Holdout set)
Mean prior absolute error
0.4589
Cross-validation details (10% Holdout set)
Weighted recall
0.7075
Cross-validation details (10% Holdout set)
Number of instances
253
Per class
Cross-validation details (10% Holdout set)
Precision
0.6976
Per class
Cross-validation details (10% Holdout set)
Predictive accuracy
0.7075
Cross-validation details (10% Holdout set)
Prior entropy
0.9463
Cross-validation details (10% Holdout set)
Relative absolute error
0.7166
Cross-validation details (10% Holdout set)
Root mean prior squared error
0.4813
Cross-validation details (10% Holdout set)
Root mean squared error
0.4315
Cross-validation details (10% Holdout set)
Root relative squared error
0.8965
Cross-validation details (10% Holdout set)
Unweighted recall
0.6537
Cross-validation details (10% Holdout set)