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')) ]) ...
- Mixture Methods
- Naive Bayes
- Meta Models
- Confusion Balancer
- Cross Validation
- Debug pipeline
- Pandas pipelines
- API Reference