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برق و الکترونیک::
دسته اعتبارسنجی
You train multiple models with various hyperparameters using the training set, you select the model and hyperparameters that perform best on the validation set, and when you're happy with your model you run a single final test against the test set to get an estimate of the generalization error.
To avoid "wasting" too much training data in validation sets, a common technique is to use cross-validation: the training set is split into complementary subsets, and each model is trained against a different combination of these subsets and validated against the remaining parts.
What is the purpose of a validation set?
What is cross-validation and why would you prefer it to a validation set?
- For example, if you are evaluating the RMSE on a validation set of 2,000 dis‐ tricts, then m = 2,000.
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