u/Rami02021

I am preprocessing a CSV dataset for multiclass classification with XGBoost. My Feature variable contain numerical and categorical values, while the target variable contain many categorical value. For example, feature variables contain patient name, phone number, and exercise history, while Target variable contain different disease name such as heart attack, stroke, Alzheimer's etc.

I know that feature variables can be encoded using one-hot encoding, but should the target variable also be encoded using the same method, or should I use a different encoding method for target variable (e.g., label encoding)?

If anyone know the answer, please let me know. I have searched everywhere, but failed to get any clear idea about it. Thank you.

I am preprocessing a CSV dataset for multiclass classification with XGBoost. My Feature variable contain numerical and categorical values, while the target variable contain many categorical value. For example, feature variables contain patient name, phone number, and exercise history, while Target variable contain different disease name such as heart attack, stroke, Alzheimer’s etc.

I know that feature variables can be encoded using one-hot encoding, but should the target variable also be encoded using the same method, or should I use a different encoding method for target variable (e.g., label encoding)?

If anyone know the answer, please let me know. I have searched everywhere,but failed to get any clear idea about it. Thank you.

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