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
Stratification means to keep the ratio of each class in each fold. So if your original dataset has 3 classes in the ratio of 60%, 20% and 20% then stratification will try to keep that ratio in each fold. In your case, X = [“hey”, “join now”, “hello”, “join today”, “join us now”, “not today”, … Read more
Use PredefinedSplit ps = PredefinedSplit(test_fold=your_test_fold) then set cv=ps in GridSearchCV test_fold : “array-like, shape (n_samples,) test_fold[i] gives the test set fold of sample i. A value of -1 indicates that the corresponding sample is not part of any test set folds, but will instead always be put into the training fold. Also see here when … Read more
In stratKFolds, each test set should not overlap, even when shuffle is included. With stratKFolds and shuffle=True, the data is shuffled once at the start, and then divided into the number of desired splits. The test data is always one of the splits, the train data is the rest. In ShuffleSplit, the data is shuffled … Read more
Trying to close this out, so am providing the answer that David and larsmans have eloquently described in the comments section: Yes, this is supposed to happen. The actual MSE is simply the positive version of the number you’re getting. The unified scoring API always maximizes the score, so scores which need to be minimized … Read more
There are some issues with your approach. To start with, you certainly don’t have to append the data manually one by one in your training & validation lists (i.e. your 2 inner for loops); simple indexing will do the job. Additionally, we normally never compute & report the error of the training CV folds – … Read more
The Wikipedia article is not wrong; according to my own experience, this is a frequent point of confusion among newcomers to ML. There are two separate ways of approaching the problem: Either you use an explicit validation set to do hyperparameter search & tuning Or you use cross-validation So, the standard point is that you … 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