I have built a simple XGBoost classifier algorithm by following closely: https://doc.dataiku.com/dss/7.0/plugins/reference/prediction-algorithms.html
However, I would like to add UI feature that allows to choose which columns to be included as Xs (features) when applying algorithm. I have a feeling that following part should include another param from json that allows to select columns (probably in fit()), but I can't find any examples in docs and github on how to implement it since I consider myself beginner in python.
This method must return a scikit-learn compatible model, ie:
- have a fit(X,y) and predict(X) methods. If sample weights
are enabled for this algorithm (in algo.json), the fit method
must have instead the signature fit(X, y, sample_weight=None)
- have a get_params() and set_params(**params) methods