Kombinasi Linier Target Data Untuk Regresi Multitarget Menggunakan Principal Component Analysis - Yonathan Purbo Santosa (Jurnal Teknologi Terpadu, 2023)
Linear regression is a method to predict numbers, a dependent variable (output) based on some independent variables (inputs). The problem with regression is that some data does not fall into linear problems. Based on this problem, RLC was invented to randomly find a correlation between output by projecting the data into the higher dimension. Unfortunately, RLC does not provide ways to inverse the projection, resulting in poor performance results. On top of that, projecting the data into a higher dimension will increase the learning algorithm complexity. Consequently, PCA can solve the problems by projecting the target data into a lower dimension while leaving possibilities for inverse transformation. This research was implemented with the help of the sci-kit-learn library to create and train the regression model and transform the dataset using Python programming language. As a result, for 12 datasets, augmentation using PCA achieved lower error in 7 datasets than RLC, averaging at 0.3270 for augmentation using PCA and 0.4003 for augmentation using RLC.