Any predictive model needs to be validated to see how it is performing on different sets of data, whether the accuracy of the model is constant over all the sources of similar data or not. This checks the problem of over-fitting, wherein the model fits very well on one set of data but doesn't fit that well on another dataset. One common method is to validate a model train-test split of the dataset. Another method is k-fold cross validation, about which we will learn more in the later chapter.
Ideally, this step should be done right at the onset of the modelling process so that there are no sampling biases in the model; in other words, the model should perform well even for a dataset that has the same predictor variables, but their means and variances are very different from what the model has been built upon. This can happen because the dataset on which the model is built (training) and the one on which it is applied (testing) can come from...
