How do I handle outliers in machine learning?

How do I handle outliers in machine learning?

Handling outliers is crucial as they can skew your model's performance. You can use methods like Z-score, IQR, or even visualizations to identify them.

What is the Z-score method?

The Z-score method standardizes your data points. If a data point's Z-score is above a certain threshold (commonly 3), it's considered an outlier.

Are there any techniques to remove outliers?

Yes, you can remove outliers based on Z-scores, IQR, or use robust models like Random Forest that are less sensitive to them.

What if I want to keep the outliers?

If you want to keep outliers, consider using models that can handle them, or analyze them separately to understand their impact.