Python Feature Scaling in SciKit-Learn (Normalization vs Standardization)
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- เผยแพร่เมื่อ 5 ส.ค. 2023
- Today we take a look at how we can apply feature scaling to data sets within scikit-learn in python. This is useful when applying Normalization or standardization to data which allows for machine learning models to perform better.
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you teach very well than other channels but i don't know why pepoles are not spend time on your channel really helpfull man
Thanks it’ll happen over time
underrated channel great video
Thanks
Helpful . Thank you so much
No problem
Excellent brother !
Thank you!
Very good video! I learned a lot. If I was to ask for more, it would be to fill in WHY normalize or standardized. You mention some about “getting your numbers in order.” Add to that there are reasons for visualization tools, comparison analysis, and whatever else. I have some ideas why, but I’m guessing as a Pandas user you have encountered many more.
Thank you for sharing.
No problem and I may make a statistics course video in the future. Just waiting on my job to apply more skills
Great video!
Thanks!
Could you also explain how the choice of feature_range affects the output processing please? Trying to understand in which case it should be (0,5) and when it should be (0,10), and how you then interpret the output, for example? Also, I am wondering: you are applying scalers to the whole dataset, but what if you have a regression type task (predicting an actual number)? If you apply scalers to all columns then your targets also change