Contents
What should be the type of categorical variable when using the function randomForest?
1 Answer. In terms of general theory, random forests can work with both numeric and categorical data. The function randomForest (documentation here) supports categorical data coded as factors, so that would be your type.
Can I use categorical variables in random forest?
Most implementations of random forest (and many other machine learning algorithms) that accept categorical inputs are either just automating the encoding of categorical features for you or using a method that becomes computationally intractable for large numbers of categories. A notable exception is H2O.
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:
What do you need to know about random forest regression?
Random Forest Regression Random forest is an ensemble of decision trees. This is to say that many trees, constructed in a certain “random” way form a Random Forest. Each tree is created from a different sample of rows and at each node, a different sample of features is selected for splitting.
How is a random forest a decision tree?
Random forest is an ensemble of decision trees. This is to say that many trees, constructed in a certain “random” way form a Random Forest. Each tree is created from a different sample of rows and at each node, a different sample of features is selected for splitting.
What is the maximum depth of a random forest?
The maximum depth of the tree is specified so as to prevent the tree from becoming too deep — a scenario that leads to overfitting. Random forest is an ensemble of decision trees. This is to say that many trees, constructed in a certain “random” way form a Random Forest.