What’s scaling?
Whenever you first load a dataset into your Python script or notebook, and check out your numerical features, you’ll likely notice that they’re all on different scales.
Which means each column or feature can have various ranges. For instance, one feature could have values starting from 0 to 1, while one other can have values starting from 1000 to 10000.
Take the Wine Quality dataset from UCI Machine Learning Repository (CC by 4.0 License) for instance.
Scaling is basically the technique of bringing all of the features closer to an identical or same range or scale, similar to transforming them so all values are between 0 and 1.
When (and why) it is advisable to scale
There are just a few explanation why scaling features before fitting/training a machine learning model is very important:
- Ensures that every one features contribute equally to the model. When one feature has a big and…
