Contents
Can RandomForest handle missing values?
The sklearn implementation of RandomForest does not handle missing values internally without clear instructions/added code. So while remedies (e.g. missing value imputation, etc.) are readily available within sklearn you DO have to deal with missing values before training the model.
Which models are not affected by Multicollinearity?
Multicollinearity does not affect the accuracy of predictive models, including regression models. The features in the x and y axis are clearly correlated; however, you need both of them to create an accurate classifier.
What to do about highly correlated variables in random forest?
You may try to increase mtry, to make sure this happens more often. You may try either recursive correlation pruning, that is in turns to remove one of two variables whom together have the highest correlation. A sensible threshold to stop this pruning could be that any pair of correlations (pearson) is lower than R 2 < .7
When the dataset has two (or more) correlated features, then from the point of view of the model, any of these correlated features can be used as the predictor, with no concrete preference of one over the others.
How to make a bivariate partial dependence plot?
I’m using randomForest in R. I can fit the forest successfully, and can produce partial dependence plots for individual variables. What I need, however, are partial dependence plots for pairs of variables – participant + others. Is this possible? Thanks very much in advance for any advice anyone can offer!
When do random sampling variables, will most of the trees grow on information a?
When random sampling variables, will most of the trees grow on information A, and as a result information B is not fully captured? That is correct, but therefore in most of those sub-samplings where variable Y was available it would produce the best possible split. You may try to increase mtry, to make sure this happens more often.