How do I handle outliers in machine learning?
An example of an AI chatbot conversation about handling 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 transformation techniques.
What is the IQR method?
The IQR method involves calculating the interquartile range (IQR) and identifying outliers as values that fall below Q1 - 1.5IQR or above Q3 + 1.5IQR.
Are there any techniques to visualize outliers?
Yes! Box plots and scatter plots are great for visualizing outliers. They can help you see how outliers compare to the rest of your data.
Should I remove outliers or keep them?
It depends on the context. If outliers are due to data entry errors, it's best to remove them. However, if they represent valid variations, consider keeping them.
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