Discover more from Daily Dose of Data Science
The Limitation of Linear Regression Which is Often Overlooked By Many
...And how to address it.
FREE 3-Day Object Detection Challenge
⭐️ Build your own object detection model from start to finish!
Hey friends! Lately, I have been in touch with Data-Driven Science. They offer self-paced and hands-on learning on practical data science challenges.
A 3-day object detection challenge is available for free. Here, you’ll get to train an end-to-end ML model for object detection using computer vision techniques.
The challenge is guided, meaning you don’t need any prior expertise. Instead, you will learn as you follow the challenge.
Also, you’ll get to apply many of my previous tips around Image Augmentation, Run-time optimization, and more.
All-in-all, it will be an awesome learning experience.
👉 Register for the challenge here: https://datadrivenscience.com/free-object-detection-challenge/.
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
robust to outliers.
It works as follows:
Select a subset of data
Fit a model
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.
Thanks for reading Daily Dose of Data Science! Subscribe for free to learn something new and insightful about Python and Data Science every day. Also, get a Free Data Science PDF (350+ pages) with 250+ tips.
👉 Tell the world what makes this newsletter special for you by leaving a review here :)
👉 If you liked this post, don’t forget to leave a like ❤️. It helps more people discover this newsletter on Substack and tells me that you appreciate reading these daily insights. The button is located towards the bottom of this email.
👉 If you love reading this newsletter, feel free to share it with friends!
👉 Sponsor the Daily Dose of Data Science Newsletter. More info here: Sponsorship details.
Find the code for my tips here: GitHub.