I am wondering is there any option in sklearn classifiers to fit using some hyperparameters and after changing a few hyperparameter(s), refit the model by saving computation (fit) cost.
Let us say, Logistic Regression is fit using C=1e5 (logreg=linear_model.LogisticRegression(C=1e5)) and we change only C to C=1e3. I want to save some computation because only one parameter is changed.