Python Feature Scaling in SciKit-Learn (Normalization vs Standardization)
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- Опубліковано 5 сер 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.
Dataset is available on my Github
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underrated channel great video
Thanks
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
learned a lot from this. excellent teaching🙌
Thanks so much
Excellent brother !
Thank you!
Helpful . Thank you so much
No problem
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
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
can you please post the jupyter notebook containing code , it will be very healpful
Will be on my website soon, I’m moving the code from the vids into articles