Can you explain the math behind robust regression?
An example of an AI chatbot conversation about robust regression and its mathematical principles.
Can you explain the math behind robust regression?
Sure! Robust regression is used to handle outliers in data. Unlike ordinary least squares, it minimizes a different loss function.
What kind of loss function does it use?
Robust regression often uses the Huber loss or the least absolute deviations, which are less sensitive to outliers.
How does the Huber loss work?
The Huber loss combines the squared loss for small errors and absolute loss for large errors, providing a balance.
Can you give me an example of when to use robust regression?
Certainly! Use robust regression when your dataset has outliers that could skew the results of ordinary regression.
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