One-hot encoding is pretty straightforward and is implemented in most software packages. There are two basic approaches to encode categorical data as continuous. Some algorithms, for example ID3 are able to handle categorical variables. X = OrdinalEncoder(cols=).fit_transform(X)
Here's an example of how lightgbm handles categories: import pandas as pdįrom category_encoders import OrdinalEncoder Catboost does an "on the fly" target encoding, while lightgbm needs you to encode the categorical variable using ordinal encoding. Lightgbm and catboost can handle categories.rpart in R can handle categories passed as factors, as explained in here.This other post comments about xgboost not handling categorical variables.However RF tends to be very robust to categorical features abusively encoded as integer features in practice. However, one of the scikit-learn developers argues thatĪt the moment it cannot. This answer reflects on decision trees on scikit-learn not handling categorical variables.ImplementationĪlthough, at a theoretical level, is very natural for a decision tree to handle categorical variables, most of the implementations don't do it and only accept continuous variables: And the fact that the variable used to do split is categorical or continuous is irrelevant (in fact, decision trees categorize contiuous variables by creating binary regions with the threshold). This will be done according to an impurity measure with the splitted branches. At every split, the decision tree will take the best variable at that moment. If the feature is contiuous, the split is done with the elements higher than a threshold. If the feature is categorical, the split is done with the elements belonging to a particular class. TheoryĮvery split in a decision tree is based on a feature. Decision trees can handle both categorical and numerical variables at the same time as features, there is not any problem in doing that.
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