Random Forest uses bootstrap sampling and feature sampling, i.e row sampling and column sampling. Therefore Random Forest is not affected by multicollinearity that much since it is picking different set of features for different models and of course every model sees a different set of data points.

Can random forest handle correlated variables?

Random forest (RF) is a machine-learning method that generally works well with high-dimensional problems and allows for nonlinear relationships between predictors; however, the presence of correlated predictors has been shown to impact its ability to identify strong predictors.

Which models can handle multicollinearity?

Perform an analysis designed for highly correlated variables, such as principal components analysis or partial least squares regression. LASSO and Ridge regression are advanced forms of regression analysis that can handle multicollinearity.