TPOT-MTR: A Multiple Target Regression Based on Genetic Algorithm of Automated Machine Learning Systems

The concept that a cross correlation might improve prediction error underpins machine learning algorithms for multi-target regression (MTR). Numerous MTR approaches have been created in recent years, however there are still uncertainties concerning how their performances impacted by dataset properties such as linearity, number of targets, and correlational complexity. In order to contribute better understanding the relationship between methods, authors proposed new model TPOT-MTR, which its result will be compared previously generated 33 synthetic datasets with controlled characteristics tested performance against other two Random Forest SVM. results demonstrated TPOT-MTR could enhance even non-linearly correlated although improvement varies depending on method regressor combinations used.