Hi, i am trying to get the algorithms and learning curves after fitting to scikit learn lr_model.fit(X, y). Just by fitting this model, it is like a black box to me. I am not sure how the decision boundary looks like and i cannot get a plot of learning curves to show whether the cost decreases for every iterations. How could i get this information out after the lr_model.fit(X, y)
Thank you
Hello @Lanying_Ma, welcome to our community!
I am afraid we can’t get the training curve from it because the sklearn’s LogisticRegression doesn’t provide such entrance. If you check out its documentation, you will see that there is a paramter called “solver”. If you check out the meaning of that parameter, it is the optimization algorithm being used to solve the problem. Those solvers’ names are what you may start googling with if you want to find out what any of them is doing.
Cheers,
Raymond
thank you so much. Will look into it