# Does R-Squared for a random forest increase as the number of predictors increases?

Does R-Squared for a random forest increase as the number of predictors increases? I have read over and over again that $R^{2}$ increases as you increase the number of predictors when the model is linear regression / ARIMA. Is this the case when using a random forest as a regression model for time series data?

I ask this because I am computing the out-of-sample $R^{2}$ score for a random forest trained on historical data and wondering if I should change to a different metric like MAPE.