We love scikit learn but very often we find ourselves writing custom transformers, metrics and models. The goal of this project is to attempt to consolidate these into a package that offers code quality/testing. This project is a collaboration between multiple companies in the Netherlands. Note that we’re not formally affiliated with the scikit-learn project at all.


LEGO® is a trademark of the LEGO Group of companies which does not sponsor, authorize or endorse this project. Also note this package, albeit designing to be used on top of scikit-learn, is not associated with that project in any formal manner.

The goal of the package is to allow you to joyfully build with new building blocks that are scikit-learn compatible.


Install scikit-lego via pip with

pip install scikit-lego

Alternatively you can fork/clone and run:

pip install --editable .


from sklego.transformers import RandomAdder

from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline


mod = Pipeline([
    ("scale", StandardScaler()),
    ("random_noise", RandomAdder()),
    ("model", LogisticRegression(solver='lbfgs'))