When to use MinMaxScaler vs StandardScaler vs something else
What is scaling?
When you first load a dataset into your Python script or notebook, and take a look at your numerical features, you’ll likely notice that they are all on different scales.
This means that each column or feature will have varying ranges. For example, one feature may have values ranging from 0 to 1, while another can have values ranging from 1000 to 10000.
Take the Wine Quality dataset from UCI Machine Learning Repository (CC by 4.0 License) for example.
Scaling is essentially the process of bringing all the features closer to a similar or same range or scale, such as transforming them so all values are between 0 and 1.
When (and why) you need to scale
There are a few reasons why scaling features before fitting/training a machine learning model is important:
- Ensures that all features contribute equally to the model. When one feature has a large and…