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Table 4 Machine learning algorithms used and a short description of their training parameters

From: The influence of negative training set size on machine learning-based virtual screening

Classifier Classification scheme Settings
Sequential Minimal Optimization (SMO) functions The complexity parameter was set at 1, the epsilon for a round-off error was 1.0 E-12, and the option of normalizing training data was chosen. The normalized polynomial kernel was used.
Naïve Bayes (NB) bayes
Instance-Based Learning (Ibk) lazy The nearest neighbor search algorithm using the Euclidean distance function and 1 neighbor.
J48 trees C.4.5 pruning
Random Forest (RF) trees Trees with unlimited depth, seed number: 1. Number of generated trees: 10.