While training an NGBoost model I got: [iter 0] loss=-2.2911 val_loss=-2.3309 scale=2.0000 norm=1.0976 [iter 100] loss=-3.3288 val_loss=-2.8532 scale=2.0000 norm=0.7841 [iter 200] loss=-4.0889 val_loss=-1.5779 scale=2.0000 norm=0.7544 [iter 300] loss=-4.8400 val_loss=8.8107 scale=2.0000 norm=0.6710 [iter 400] loss=-5.4463 val_loss=51.7171 scale=2.0000 norm=0.5999 It looks like overfit occurred between iterations 100 and 200. Is the best (val_loss wise) model saved, or did I get the last one reported (with a massive overfit, -5.4463 in train loss vs 51.7171 in validation loss)? If I really do get …

Recently I have been doing some research on NGBoost, but I could not see any parameter for categorical features. Is there any parameter that I missed? __init__(self, Dist=<class 'ngboost.distns.normal.Normal'>, Score=<class 'ngboost.scores.MLE'>, Base=DecisionTreeRegressor(ccp_alpha=0.0, criterion='friedman_mse', max_depth=3, | max_features=None, max_leaf_nodes=None, | min_impurity_decrease=0.0, min_impurity_split=None, | min_samples_leaf=1, min_samples_split=2, | min_weight_fraction_leaf=0.0, presort='deprecated', | random_state=None, splitter='best'), natural_gradient=True, n_estimators=500, learning_rate=0.01, minibatch_frac=1.0, verbose=True, verbose_eval=100, tol=0.0001) https://github.com/stanfordmlgroup/ngboost