Examples of using Cross-validation in English and their translations into Chinese
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In another case, say, your training set error is low but your cross-validation set error is high: E_train is low and E_cv is high.
Leave one out cross-validation results.
In cross-validation, the training data is partitioned.
Besides cross-validation, there are many methods to combat overfitting.
Cross-validation is the best way to evaluate models used for prediction.
A good k can be selected by various heuristic techniques like cross-validation.
A good k can be selected by various heuristic techniques like cross-validation.
Cross-validation can be used to compare the performances of different predictive modeling procedures.
A good k can be selected by various heuristic techniques, e.g. cross-validation.
Cross-validation allows you to tune parameters with only your original training set.
A good k can be selected by various heuristic techniques, e.g. cross-validation.
If you use cross-validation, compare the test R2 to the predicted R2.
Nested cross-validation(do feature selection on one level, then run entire method in cross-validation on outer level).
Cross-validation: There are various methods to check the accuracy of supervised models on unseen data.
A solution to this problem is a procedure called cross-validation(CV for short).
Cross-validation: There are various methods to check the accuracy of supervised models on unseen data.
Cross-validation: There are various methods to check the accuracy of supervised models on unseen data.
A good k can be selected by various heuristic techniques, for example, cross-validation.
A good k can be selected by various heuristic techniques, for example, cross-validation.
Which cross-validation technique you would use on the time series data set, k-fold or LOOCV?