Table 7.

Performance evaluation results.

Algorithm	1	2	3	4	5	6	7	8	9	10	11	12	13
Random forest	61.43	62.86	64.29	61.43	62.86	62.86	64.23	60.00	58.57	60,00	62.86	58.57	54.23
Adaboost	71.43	71.43	71.43	71.43	71.43	71.43	71.43	71.43	71.43	71.43	71.43	71.43	70,00
Decision tree	60.00	58.57	58.57	58.57	58.57	58.57	58.57	58.57	57.14	74.29	62.86	54.29	58.57

Experiment with attributes removed from the training set. Performance evaluation results considering the Validation set of ML algorithms—Correctly Classified Instances—Accuracy. Combination of Attributes in the Training Set Algorithm 1 2 3 4 5 6 7 8 9 10 11 12 13 Random forest, Adaboost and Decision tree. We observe that Decision Tree obtained the best result for accuracy (74.29) in experiment 10.