Hi, I made a “simple” python script with embedding layer, skip connection… for tabular data, with usual API function for a multi classification target and I have tried to connect tfdf.keras.GradientBoostedTreesModel to one of the last layer (before the softmax) to see if it can improves the result. It works, and when you compare the Neural Network alone and the NN + Gradient BoostedTrees, you get always a better logloss result. But when you don’t import tensorflow_decision_forests as tfdf the NN gets better results than in the first case, even comparing to NN+Gradient. I made several tests and the difference is quite significant and reproducible. Weird ? it seems that tf 2.5 get worse resuts  comparing to 2.4.1 (script without tfdf) ?