Interpreting Tabular Data Results

I am currently busy with Part 1, Lesson 4 - the tabular data section.

I was wondering, are there factory methods or standard ways to interpret the results?

Specifically, I want to know which variables are most useful (or correlate most highly with) the dependant variable that you are trying to predict? Apologies in advance if this is a dumb question!

What you want is feature or permutation importance (the algorithm). I have a notebook here detailing it in fastai version 1 and there’s a very long forum thread with a good discussion on it, do a quick search :slight_smile:

Ah this is awesome, will look into it. Thanks a bunch!

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Hi, @muellerzr
How to deal with very little data?
example:Covid19 dataset
Suppose i have hardly 30 rows, in such a case deep learning can’t be used for the tabular data.
What are the other option? Xgboost or random forest or maybe SGD ?


Edit: Xgboost also couldn’t get an accuracy