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Amazon Machine Learning
Developer Guide (Version Latest)

Evaluating Model Accuracy

The goal of the ML model is to learn patterns that generalize well for unseen data instead of just memorizing the data that it was shown during training. Once you have a model, it is important to check if your model is performing well on unseen examples that you have not used for training the model. To do this, you use the model to predict the answer on the evaluation dataset (held out data) and then compare the predicted target to the actual answer (ground truth).

A number of metrics are used in ML to measure the predictive accuracy of a model. The choice of accuracy metric depends on the ML task. It is important to review these metrics to decide if your model is performing well.