Contents
Which is better the validation score or the training score?
For the naive Bayes, both the validation score and the training score converge to a value that is quite low with increasing size of the training set. Thus, we will probably not benefit much from more training data. In contrast, for small amounts of data, the training score of the SVM is much greater than the validation score.
Why is my Train score higher than my Test score?
Once you find a good value using this cross-validation set, then try again with your test set, and you should see that the test result is better. The error during training will actually be worse . . . but usually that’s a good sign that you are getting a model that generalises well, when training, c.v. and test errors are similar.
Why is my validation loss lower than my training loss?
Regularization methods often sacrifice training accuracy to improve validation/testing accuracy — in some cases that can lead to your validation loss being lower than your training loss. Secondly, keep in mind that regularization methods such as dropout are not applied at validation/testing time.
Is it normal to have high training and low test scores?
This is a normal symptom of over-fitting and is not the least bit strange. Errors normally get worse between training and test, but your dramatic shift from 100% accuracy on training to 40% accuracy on test is a large gap.
What’s the difference between a validation and a test set?
Generally, the term “validation set” is used interchangeably with the term “test set” and refers to a sample of the dataset held back from training the model. The evaluation of a model skill on the training dataset would result in a biased score.
What does high training score and low test score mean?
Usually, high training score and low test score is over-fitting. Very low training score and low test score is under-fitting. First example here, in technical term is called low bias and high variance which is over-fitting. The latter example, high variance and high bias called under-fitting.
When to divide data into training and validation?
If the test set is locked away, but you still want to measure performance on unseen data as a way of selecting a good hypothesis, then divide the available data (without the test set) into a training set and a validation set.