2.3. Four Machine Learning Classifiers and a Conventional Logistic Regression Model
NB
REGRESSION
In this study, we built four machine learning models (extreme gradient boosting (XGBoost), support vector machine (SVM), naive Bayes (NB), and random forest (RF)) and a conventional logistic regression (LR) model using the caret package for R language (version 6.0-90) to diagnose dMMR discriminatively. An analysis of colinearity was performed on the initial 23 variables to exclude significantly correlated variables. Subsequently, least absolute shrinkage and selection operator (LASSO) regression and RF were used for variable selection. LASSO regression is known to be able to remove unimportant variables via the regression coefficients penalizing the size of the parameters. Applying the LASSO regression method, feature selection and predictive signature building were done. LASSO regression shrinks the coefficient estimates toward zero, with the degree of shrinkage dependent on an additional parameter, 