Does random forest take categorical variables?
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.
What is a categorical random variable?
Categorical random variables are normally described statistically by a categorical distribution, which allows an arbitrary K-way categorical variable to be expressed with separate probabilities specified for each of the K possible outcomes. Categorical variables that have only two possible outcomes (e.g., “yes” vs.
How to use random forest for categorical variables with?
Each categorical variable has multiple levels ( some of them having 20 levels) 3. Some of the features have data missing Can random forests work without imputation of these missing values. If no then what is the best way to impute these missing categorical values.
When to use conditional random forests in R?
If you work with variables that have different number of levels or if you work with a mix of variables that are both continuous and categorical use conditional random forests instead of standard random forests. The latter will be biased. There are packages in R for performing conditional RF.
Is it possible to use randomForest in scikit learn?
As has been noted, it depends on the implementation. Scikit-learn requires one-hot (or it did last time I checked), and R’s randomForest can do with either. You’ve got to be careful, though, in general, dealing with categorical variables and random forests. Especially those with many levels.
Can a random forest work without missing values?
Can random forests work without imputation of these missing values. If no then what is the best way to impute these missing categorical values. Any literature or R functionality which addresses this issue will be really helpful