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The final step of this pipeline is to assess how good our model is through performance evaluation. Evaluation of predictive models is one of the most crucial steps in the pipeline. The basic idea is to develop the model using some training samples, but test this train model on some other unseen samples, ideally from future data. It is important to note that the training error is not very useful, because you can very easily over fit the training data by using complex models which do not generalize well to future samples. Testing error is the key metric because its a better approximation of the true performance of the model on future samples. The classical approach for evaluation is through cross-validation process or CV. The main idea behind cross-validation is to iteratively split a data set into training and validation sets. And we want to view the model on the training set, and test the model on the validation step, but do this iteratively, many times.