Dealing with numerical features
In terms of numerical features (discrete and continuous), you can think of transformations that rely on the training data and others that rely purely on the (individual) observation being transformed.
Those who rely on the training data will use the training set to learn the necessary parameters during fit, and then use them to transform any test or new data. The logic is pretty much the same as what you just learned for categorical features; however, this time, the encoder will learn different parameters.
On the other hand, those that rely purely on (individual) observations do not depend on training or testing sets. They will simply perform a mathematical computation on top of an individual value. For example, you could apply an exponential transformation to a particular variable by squaring its value. There is no dependency on learned parameters from anywhere – just get the value and square it.
At this point, you might be thinking...
