[michaelscott]

I worked on a little side project for classification on tabular data, but a really challenging use case where the data was prone to a lot of noise and some randomness in the dependent variable. Tree models couldn't get a high enough accuracy, and when the dataset was under roughly 6k entries deep learning performed even worse (as expected).

What was really interesting was when the dataset had more than 6k or so; the deep learning model was suddenly much more accurate and by a wide gap! At roughly the 10k mark, the DL model was outperforming the tree model easily.