Contents
Why is random forest robust?
Random forests is great with high dimensional data since we are working with subsets of data. It is faster to train than decision trees because we are working only on a subset of features in this model, so we can easily work with hundreds of features.
Does Multicollinearity affect random forest?
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. Feature importance will definitely be affected by multicollinearity.
What can random forest be used for in regression?
In the case of random forest, it ensembles multiple decision trees into its final decision. Random forest can be used on both regression tasks (predict continuous outputs, such as price) or classification tasks (predict categorical or discrete outputs).
How are missing values replaced in random forests?
Random forests has two ways of replacing missing values. The first way is fast. If the mth variable is not categorical, the method computes the median of all values of this variable in class j, then it uses this value to replace all missing values of the mth variable in class j.
How is the random forest a decision tree?
Random Forest The random forest is a model made up of many decision trees. Rather than just simply averaging the prediction of trees (which we could call a “forest”), this model uses two key concepts that gives it the name random: Random sampling of training data points when building trees
Are there any values outside the training set in random forest?
There are no values outside that range. Random Forest cannot extrapolate. As you have seen above, when using a Random Forest Regressor, the predicted values are never outside the training set values for the target variable. If you look at prediction values they will look like this: