Using Min Max Scaler to scale features | Machine Learning скачать в хорошем качестве

Using Min Max Scaler to scale features | Machine Learning

In this tutorial, we'll look at Min Max Scaler, a type of feature scaling technique for linear Machine Learning models. In the tutorial, we'll be going through all the nitty-gritties of Min Max Scaler, when to use them, when NOT to use them, how is it helpful, how is it NOT so helpful etc etc. 0:00 Intro 3:24 Python code Feature scaling is so important that your model performance could shoot up by many a percentage points if you use the correct feature scaling techniques. In a nutshell, Min Max Scaler works by subtracting the minimum value, and dividing by the difference between the max and min values in a particular feature for each observation so as to scale the values to be in a range of 0 and 1 always. I've uploaded all the relevant code and datasets used here (and all other tutorials for that matter) on my github page which is accessible here: Link: https://github.com/rachittoshniwal/ma... If you like my content, please do not forget to upvote this video and subscribe to my channel! If you have any qualms regarding any of the content here, please feel free to comment below and I'll be happy to assist you in whatever capacity possible. Thank you!