Table 1

Selected parameters for KNN, NN, and random forest algorithms

AlgorithmThe selected combination of parametersComments
KNNk =15The number of closest neighbors for classification 1, 2 and 3
k =20The number of closest neighbors for classification 4 and 5
NNn_hidden = 3The number of hidden layers
h_size = (50, 50, 50)Hidden layer size
φ = tanhActivation function [logistic, tahn, relu]
α = 0.01Learning rate
Random forestmax_depth = 100The maximum depth of the tree
max_features = sqrtThe number of features to consider when searching for a suitable split
min_samples_split = 2The minimum number of samples required to split an internal node
n_estimators = 100The number of trees in the forest

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