Sklearn Pipelines for the Modern

 

Sklearn Pipelines for the Modern ML Engineer: 9 Techniques You Can’t Ignore

Automatically isolates numerical and categorical features of X.

Imputes missing values in numeric features.

Log-transforms skewed features while normalizing the rest.

Imputes missing values in categorical features and one-hot encodes them.

Normalizes the target array y for good measure.

Apart from collapsing almost 100 lines worth of unreadable code into a single line, awesome_pipeline can now be inserted into cross-validators or hyperparameter tuners, guarding your code from data leakage and making everything reproducible, modular, and headache-free.

Let’s see how to build the thing.

0. Estimators vs transformers

First, let’s get the terminology out of the way.

A transformer in Sklearn is any class or function that accepts features of a dataset, applies transformations, and returns them. It has fit_transform and transform methods.

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