I find Tensorflow decision forest has nearly 2x slower training time than alternative libraries when I call .fit from within a Colab notebook. It also seems to hang for a long time after the Keras progress bar reaches 100%.
In this 11 example I get the following results on 250k rows, and 200 features:
tfdf 5min 49s
xgboost (tree_method = ‘approx’) 3min 11s
lightgbm 46.1 s
xgboost (tree_method = ‘histogram’) 30.4 s
(1) Does anyone here have an understanding of why tfdf is significantly slower than xgboost on the same dataset, with similar parameters, and any tips on steps that can be taken to debug or speed up? I’m posting the Colab example because it’s easiest to share but I get similar magnitudes of differences internally, on larger datasets.
(2) Is there any plan to offer histogram-based-binning options for TFDF? It does seem to dramatically reduce training time which would be very helpful to potential users with large datasets.