Data Mining - (Test|Expected|Generalization) Error
1 - About
Test error is the prediction error that we incur on new data. The test error is actually how well we'll do on future data the model hasn't seen.
The test error is the average error that results from using a statistical learning method to predict the response on a new observation, one that was not used in training the method.
2 - Articles Related
3 - Estimation
Test error can be estimated :
- indirectly by making an adjustment to the training error to account for the bias due to overfitting (ie computing the training error and then adjusting it). The following methods adjust the training error in order to give us an estimate of the test error.
- directly, using either a validation set approach or a cross-validation approach (I fit models on part of the data, and then I evaluate them on a holdout set.)