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Can true error be less 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 difference between loss and error?
An error function measures the deviation of an observable value from a prediction, whereas a loss function operates on the error to quantify the negative consequence of an error.
What’s the point of a low training error?
Low training error can be indicative of overfitting.. is that the only use of it? Training error by itself can be a very bad metric of your model performance, as you have correctly pointed out. However, there is no going around the fact that you need to train your model to make some meaningful predictions.
How are training error and test error used in machine learning?
Training error vs test error 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).
When to use k = 1 for training error?
When K = 1, you’ll choose the closest training sample to your test sample. Since your test sample is in the training dataset, it’ll choose itself as the closest and never make mistake. For this reason, the training error will be zero when K = 1, irrespective of the dataset.
When is the training error zero in KNN?
For this reason, the training error will be zero when K = 1, irrespective of the dataset. There is one logical assumption here by the way, and that is your training set will not include same training samples belonging to different classes, i.e. conflicting information. Some real world datasets might have this property though.