I have a logistic regression model with a defined set of parameters (warm_start=True).
As always, I call LogisticRegression.fit(X_train, y_train) and use the model after to predict new outcomes.
Suppose I alter some parameters, say, C=100 and call .fit method again using the same training data.
Theoretically, for the second time, I think .fit should take less computational time as compared to the model with warm_start=False. However, empirically is not actually true.
Please, help me understand the concept of warm_start parameter.
P.S.: I have also implemented
SGDClassifier()for an experimentation.