What are the different types of robust regression?
Robust regression techniques are designed to be less sensitive to outliers. Common types include Huber regression, RANSAC, and Theil-Sen estimator.
Can you explain Huber regression in more detail?
Sure! Huber regression combines the ideas of least squares and least absolute deviations. It uses a quadratic loss for small errors and a linear loss for large errors, making it robust to outliers.
What about RANSAC? How does it work?
RANSAC, or Random Sample Consensus, iteratively selects random subsets of data to fit a model and identifies inliers, effectively ignoring outliers.
Are there any specific use cases for these methods?
Yes, robust regression methods are often used in fields like finance, engineering, and environmental science, where data can be noisy or contain outliers.
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