Kaggler 0.8.0 is released. It added
model.AutoLGB for automatic feature selection and hyper-parameter tuning using
The implementation is based on the solution of the team AvengersEnsmbl at the KDD Cup 2019 Auto ML track. Details and winners’ solutions at the competition are available at the competition website.
model.BaseAutoML is the base class, from which you can inherit to implement your own auto ML class.
model.AutoLGB is the auto ML class for LightGBM. It’s simple to use as follows:
from kaggler.model import AutoLGB model = AutoLGB(objective='binary', metric='auc') model.tune(X_trn, y_trn) model.fit(X_trn, y_trn) p = model.predict(X_tst)
Other updates include:
.travis.ymlfor Travis CI
Any comments or contributions will be appreciated.