Is cross-validation error a reasonable estimate of testing error?

Is cross-validation error a reasonable estimate of testing error?

Cross-Validation is a technique used in model selection to better estimate the test error of a predictive model. The idea behind cross-validation is to create a number of partitions of sample observations, known as the validation sets, from the training data set.

What if test error is less than train error?

If your test error is less than the training error, this means that there is a sampling bias in your test. This does not mean that you know the whole subject, just that the test was ‘biased’ for you.

How is cross-validation error calculated?

1 Answer

  1. The most common way of calculating overall error for cross validation is to pool the predictions of all folds and then calculate the error of the pooled predictions.
  2. The answer you link instead pools the errors obtained for the individual surrogate models.

Can validation error be lower than training error?

Generally speaking though, training error will almost always underestimate your validation error. However it is possible for the validation error to be less than the training. You can think of it two ways: Your training set had many ‘hard’ cases to learn.

What is the one standard error rule?

One standard error is a heuristic rule. If one number is within one standard error from the other number, the two numbers can be thought of as statistically equal.

Does cross validation reduce Type 2 error?

The 10-fold cross-validated t test has high type I error. However, it also has high power, and hence, it can be recommended in those cases where type II error (the failure to detect a real difference between algorithms) is more important.

Why is validation error higher than test error?

Validation Set: this data set is used to minimize overfitting, and adjust the weights to retrain. Test error is consistently higher than training error: if this is by a small margin, and both error curves are decreasing with epochs, it should be fine.

What does it mean when validation loss is less than training loss?

Reason #2: Training loss is measured during each epoch while validation loss is measured after each epoch. The second reason you may see validation loss lower than training loss is due to how the loss value are measured and reported: Training loss is measured during each epoch.

Why would test error be higher than validation error?

A testing error significantly higher than the training error is probably an indication that your model is overfitting. Introducing regularization to your modelling could help, or possibly just reducing the number of free parameters.

Why is test error higher than train error?

Test error is consistently higher than training error: if this is by a small margin, and both error curves are decreasing with epochs, it should be fine. However if your test set error is not decreasing, while your training error is decreasing alot, it means you are over fitting severely.

What is another name for standard error?

What is another word for standard error?

standard deviation deviation
normal deviation predictable error
probable error range of error

Is the test _ set unseen in cross validation?

This is the correct approach. As a rule, you should only train your model using training data. Thus the test_set should remain unseen in the cross-validation process, i.e. by the model’s hyperparameters, otherwise you could be biasing the results obtained from the model by adding knowledge from the test sample.

Why is cross validation error high upon overfitting?

The more variables you include in your model, the lower the training error will get. However, doing so results in overfitting because your model becomes too specialized to its training data that when unseen data comes along it will instead perform worse.

Do you need to call the FIT method separately while using cross validation?

We do not need to call the fit method separately while using cross validation, the cross_val_score method fits the data itself while implementing the cross-validation on data. Below is the example for using k-fold cross validation.

Is the error displayed by cross Val only from the training data?

Yes, the error displayed by cross_val_score will be only from the training data. So the idea is that once you are satisfied with the results of cross_val_score, you fit the final model with the whole training set, and perform a prediction on y_test.