What are training errors?
There are two important concepts used in machine learning: the training error and the test error. Training Error: We get the by calculating the classification error of a model on the same data the model was trained on (just like the example above).
Why is training error higher than validation error?
Simply put, if training loss and validation loss are computed correctly, it is impossible for training loss to be higher than validation loss. This is because back-propagation DIRECTLY reduces error computed on the training set and only INDIRECTLY (not even guaranteed!) reduces error computed on the validation set.
How is a training error different from a test error?
It is very important to understand the difference between a training error and a test error. Remember that the training error is calculated by using the same data for training the model and calculating its error rate. For calculating the test error, you are using completely disjoint data sets for both tasks.
How to prevent model errors in machine learning?
Since the consequences are often dire, I’m going to discuss how to prevent mistakes in model validation and the necessary components of a correct validation. To kick off the discussion, let’s get grounded in some of the basic concepts of validating machine learning models: predictive modeling, training error, test error and cross validation
Why is training set should always be smaller than test set?
With larger datasets, any observable estimate in a sample becomes very close to its value on the population it has been drawn from. Larger test datasets ensure a more accurate calculation of model performance. Training on smaller datasets can be done by sampling techniques such as stratified sampling.
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.