Closed Rabea007 closed 1 year ago
anybody ?
Hi @Rabea007 , Apologies for the very late reply. The issue is that XCM model was originally designed for classification tasks. It's gradcam module specifically shows how read datapoint impacts the probability of a sample on a particular target class. This is not possible with regression tasks.
As an alternative, you might find learn.feature_importance or learn.step_importance useful. Both support classification, regression and forecasting tasks. (docs)
Closed due to lack of response.
Hi ,
I managed to use show_gradcam func on classification model and it worked well. But when I use it with regression models (through [TSRegression()] tfms):
(1) when yb passed as scalar:
model.show_gradcam(xb[0], yb[0])
I get this error:(2) when yb passed as vector:
model.show_gradcam(xb, yb)
Do you know why this error is happening especially in the regression case and not in classification ? Is there any possible way to fix it ?