How should I encode both target and feature variable for a multiclass classification? [D]
📰 Reddit r/MachineLearning
Learn how to encode target and feature variables for multiclass classification with XGBoost, handling numerical and categorical values
Action Steps
- Encode categorical feature variables using One-Hot Encoding or Label Encoding
- Use Label Encoding for the target variable to convert disease names into numerical labels
- Scale numerical feature variables using Standard Scaler or Min-Max Scaler to ensure consistency
- Apply XGBoost classifier to the preprocessed dataset
- Evaluate the model's performance using metrics such as accuracy, precision, and recall
Who Needs to Know This
Data scientists and machine learning engineers working on classification problems with mixed data types will benefit from this lesson, as it covers encoding strategies for both feature and target variables
Key Insight
💡 Proper encoding of categorical variables is crucial for multiclass classification problems, and using the right encoding strategy can significantly impact model performance
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🤖 Encoding target and feature variables for multiclass classification with XGBoost? Use One-Hot Encoding, Label Encoding, and scaling to prep your data! 📊
Key Takeaways
Learn how to encode target and feature variables for multiclass classification with XGBoost, handling numerical and categorical values
Full Article
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
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