What should be the categorical variable when using the function random forest?
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 categorical variables be used in random forest?
One advantage of decision tree based methods like random forests is their ability to natively handle categorical predictors without having to first transform them (e.g., by using feature engineering techniques).
How can I fit categorical data types for random forest?
In my work, before fitting my data for any types of classification method I use Label encoder for converting value and before converting I ensure that no blank space exist in my data set. Yes, you can numericalize with df.category_name.codes but you will see like 1 or 0 or -1 so you have to write a function like this.
Which is worse random forest or tree based ensembles?
It turns out this is not true for all models and to my surprise, random forest performed consistently worse for datasets with high cardinality categorical variables. Being a Kobe Bryant fan, I decided to use his shot selection data to build a model to predict whether a shot given the circumstances would land in or not.
Which is better random forest or hot encoding?
In general, one hot encoding provides better resolution of the data for the model and most models end up performing better. It turns out this is not true for all models and to my surprise, random forest performed consistently worse for datasets with high cardinality categorical variables.
How are categorical variables disadvantaged in decision trees?
The resulting tree from a dataset containing a majority of continuous variables that would look like something in the figure to the left. Categorical variables are naturally disadvantaged in this case and have only a few options for splitting which results in very sparse decision trees.