What are the methods of robust regression?
An example of an AI chatbot conversation about robust regression techniques.
What are the methods of robust regression?
Robust regression methods are designed to be less sensitive to outliers in data. Common methods 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 is RANSAC and how does it work?
RANSAC, or Random Sample Consensus, is an iterative method that estimates parameters of a mathematical model from a dataset containing outliers. It randomly selects a subset of the data, fits a model, and checks how many points fit this model well.
Are there any limitations to these methods?
Yes, while robust regression methods are better with outliers, they can still be affected by high leverage points and may require careful tuning of parameters.
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