After watching Jeremy’s examples of model interpretation in machine learning course (lesson 3 and lesson 4) I’ve wondered if something similar exists in tabular models. And looks like it didn’t, even though it seems to be very tempting to understand data through it’s model interpretation.
So I’ve implemented these functions and made an example for the Rossmann data.
There you can find functions for:
- feature importance calculations
- partial dependence calculation
- plotting dendrograms (for data inself)
- plotting embeddings
- also there is an example of using trained embeddings for another models (using only, not retraining), like to compare NN with Random Forest+embeddings
The notebook is here