Table 1. Optimized Parameters for Each Algorithm.

algorithm	optimized parameters	unbalanced TS	equal size models
RF	split criterion	gini index
	attribute sampling	square root
	set of attributes for each tree	different
	number of trees	423
	tree depth	6
	equal size sampling	yes
kNN	number of neighbors to consider	5	7
	weight neighbors by distance	yes	no
GB	number of trees	280	100
	learning rate	0.98	1
	attribute sampling	square root
	set of attributes for each tree	same
	maximum tree depth	8
XGB	eta	0.589	0.28
	boosting rounds	253	100
	gamma	0.182
	lamba	4.842
	alpha	0.211
	maximum depth	6