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برق و الکترونیک::
خطای تعمیم
The error rate on new cases is called the generalization error (or out-of- sample error), and by evaluating your model on the test set, you get an estimation of this error.
, your model makes few mistakes on the training set) but the generalization error is high, it means that your model is overfitting the train‐ ing data.
Suppose you find the best hyperparame‐ ter value that produces a model with the lowest generalization error, say just 5% error.
The problem is that you measured the generalization error multiple times on the test set, and you adapted the model and hyperparameters to produce the best model for that set.
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.
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