Understand Captum.ai better and how we want to utilize it.

[iamgiddyaboutgit]

https://captum.ai/docs/attribution_algorithms

[jcoffsky3]

We probably want to focus on the primary attribution algorithms.

Homework uses the following code. We can adapt something similar:

from captum.attr import IntegratedGradients

model.eval()
integrated_gradients = IntegratedGradients(model)
ig = integrated_gradients.attribute(sample_features)
ig = torch.mean(ig, dim=1)
igs = ig.flatten().tolist()

We can then plot igs to see feature importance. Maybe (hopefully?) we will see something similar to the homework where certain features near each other on the plot have high importance, and we could reorder the features along the axis to highlight this

[iamgiddyaboutgit]

Sounds good! I need your statistical expertise here. Say Captum identifies a bunch of features that are important for the classification. Upon further analysis, we discover that a handful of these are also associated with Type 2 Diabetes Mellitus. How can we test that these double associations were unlikely to have occurred by chance?

[jcoffsky3]

That would normally be through hypothesis testing, I'm thinking we could use a proportion z test but we can look into it more after we have the captum results.