title: Overcoming Multicollinearity in Random Forest Regression and still keeping all variables in the model
body: <p>I am new to Random Forest Regression. I have 300 Continuous variables ( 299 predictors and 1 target)in prep1, where some predictors are highly correlated. The problem is that I still need to get the importance value <strong>for each one of the predictors</strong>, so eliminating some is not an option. </p>
  
  <p>Here are my questions:</p>
  
  <p>1) Is there a way to choose for each tree only variables that are not highly correlated, if yes, how should the below code be adjusted?</p>
  
  <p>2) Assuming yes to 1), will this take care of the multi-collinearity problem?</p>
  
  <pre><code>  bound &lt;- floor(nrow(prep1)/2)         
    df &lt;- prep1[sample(nrow(prep1)), ]            
    train &lt;- df[1:bound, ]             
    test &lt;- df[(bound+1):nrow(df), ]    
    modelFit &lt;- randomForest(continuous_target ~., data = train)
    prediction &lt;- predict(modelFit, test)  
  </code></pre>