Categorical | XGBoosting

Helpful examples for preparing categorical data for XGBoost models.

Categorical data for XGBoost models refers to input or output variables that represent discrete categories or groups, which must be encoded into numerical values using techniques like one-hot encoding or ordinal encoding before they can be used by the model for training and prediction.

Examples	Tags
Configure XGBoost "enable_categorical" Parameter	Parameters Categorical
Configure XGBoost "max_cat_threshold" Parameter	Parameters Categorical
Configure XGBoost "max_cat_to_onehot" Parameter	Parameters Categorical
Configure XGBoost "use_label_encoder" Parameter	Parameters Classification Categorical
Encode Categorical Features As Dummy Variables for XGBoost	Data Categorical
Label Encode Categorical Input Variables for XGBoost	Data Categorical
Label Encode Categorical Target Variable for XGBoost	Data Categorical
One-Hot Encode Categorical Features for XGBoost	Data Categorical
Ordinal Encode Categorical Features for XGBoost	Data Categorical
String Input Features for XGBoost	Data Categorical
XGBoost Compare "max_cat_threshold" vs "max_cat_to_onehot" Parameters	Parameters Categorical
XGBoost Don't Use One-Hot-Encoding	Data Categorical
XGBoost Native Categorical Faster Than One Hot and Ordinal Encoding	Performance Data Categorical
XGBoost's Native Support for Categorical Features	Data Categorical