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Table 2 Results on simulated data: experiments were done using increasing number of features.

From: A hierarchical Naïve Bayes Model for handling sample heterogeneity in classification problems: an application to tissue microarrays

   HierNB Classifier StNB Classifier
Exp N Feat. Acc Brier Acc Brier
1 1 0.925 [0.917, 0.933] 0.112 0.874 [0.864, 0.884] 0.184
2 2 0.966 [0.960, 0.971] 0.052 0.921 [0.912, 0.929] 0.118
3 3 0.987 [0.983, 0.990] 0.020 0.946 [0.938, 0.952] 0.082
4 10 0.998 [0.997, 0.999] 0.002 0.985 [0.981, 0.989] 0.023
  1. Exp = number of experiment, N. Feat = Number of features, Acc = Accuracy, Brier = Brier Score. In brackets the 95% confidence intervals for the estimate of the accuracy.