GridSearchCV as @Gauthier Feuillen said is used to search best parameters of an estimator for given data. Description of GridSearchCV:- gcv = GridSearchCV(pipe, clf_params,cv=cv) gcv.fit(features,labels) clf_params will be expanded to get all possible combinations separate using ParameterGrid. features will now be split into features_train and features_test using cv. Same for labels Now the gridSearch estimator … Read more
All estimators in scikit where name ends with CV perform cross-validation. But you need to keep a separate test set for measuring the performance. So you need to split your whole data to train and test. Forget about this test data for a while. And then pass this train data only to grid-search. GridSearch will … Read more
[Answer after the question was edited & clarified:] Before rushing into implementation issues, it is always a good practice to take some time to think about the methodology and the task itself; arguably, intermingling early stopping with the cross validation procedure is not a good idea. Let’s make up an example to highlight the argument. … Read more
Yes, it can be done, but with imblearn Pipeline. You see, imblearn has its own Pipeline to handle the samplers correctly. I described this in a similar question here. When called predict() on a imblearn.Pipeline object, it will skip the sampling method and leave the data as it is to be passed to next transformer. … Read more
This is called as nested cross_validation. You can look at official documentation example to guide you into right direction and also have a look at my other answer here for a similar approach. You can adapt the steps to suit your need: svr = SVC(kernel=”rbf”) c_grid = {“C”: [1, 10, 100, … ]} # CV … Read more