Hello, at the moment Im doing course C1 week2 Lab03 Feature Scaling and Learning Rate.

I found super useful to run the gradient descent function and observe how values change for the Cost function and for the parameters w in each iteration. This historical information per each iteration I believe provide insightful information about if gradient descent is converging: if Cost is close to 0 and if parameters Ws values are not changing from previous iteration.

My question is: is possible to get above historical info from sklearn model SGDRegressor? How I can get from SGDRegressor Ws and Cost values per each interaction?

Many thanks