2.3.3. QUEST Algorithm
QUEST stands for a quick, unbiased, efficient statistical tree and is a relatively new algorithm for binary tree increasing. This deals with split field selection and different split-point selection. In Search, the univariate split conducts roughly unbiased field selection. If all the predictor fields are equally informative concerning the target field, QUEST selects any of the predictor fields with equal likelihood. QUEST offers many of Classification and Regression Trees’ (C&RT) benefits. Still, the trees can become unusable, like C&RT. Automatic cost-complexity pruning can be applied to the QUEST tree to reduce its scale. Concerning this study, the boosting method was used to build the QUEST model to enhance accuracy (11). The hyperparameters of the SVM algorithm were maximum tree depth of 5, maximum surrogates of 5, minimum records in parent branch of 2%, minimum records in child branch of 1%, and the number of component models for boosting was ten. The model hyperparameters were optimized by SMO approximation (13).