What are robust statistics and why are they important in machine learning?

What are robust statistics and why are they important in machine learning?

Robust statistics are methods that provide reliable results even when assumptions about the data are violated. In machine learning, they help improve model performance by reducing the influence of outliers.

Can you give me some examples of robust statistical methods?

Sure! Some examples include the median, trimmed means, and robust regression techniques like Huber regression.

How do robust statistics differ from traditional statistics?

Robust statistics focus on providing accurate results despite outliers or non-normal distributions, while traditional statistics often assume data follows a specific distribution.

When should I use robust statistics in my machine learning projects?

You should consider using robust statistics when your data contains outliers, is skewed, or when you want to ensure your model is not overly sensitive to small changes in the data.