The Limitation of Linear Regression Which is Often Overlooked By Many
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The Limitation of Linear Regression Which is Often Overlooked By Many
...And how to address it.
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Let’s get to today’s post now.
Linear Regression is the most widely used ML algorithm.
But it is sensitive to outliers.
In fact, even a few outliers can significantly impact Linear Regression performance.
Instead, try RANSAC Regression. It is
non-deterministic,
iterative, and
robust to outliers.
It works as follows:
Select a subset of data
Fit a model
Calculate residuals
Classify points as outliers/inliers based on thresholds applied to residuals
Repeat (until max iterations or when a condition is met)
As shown above, while Linear Regression is influenced by outliers, RANSAC Regression isn't.
Nonetheless, it is always recommended to experiment with many robust methods and see which one fits your data best.
Having said that, what are some other popular models that are robust to outliers? Let me know :)
👉 Get started with RANSAC: Sklearn Docs.
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Find the code for my tips here: GitHub.
I like to explore, experiment and write about data science concepts and tools. You can read my articles on Medium. Also, you can connect with me on LinkedIn and Twitter.