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hi guys I hope youre doing very well in this video were going to learn two techniques to estimate the test error why do we want to estimate the test error well basically because we would like to select the model that makes the best predictions in new situations and that is the same as same that that we would like the model with the lowest test error and thats why we want to have a method that allows us to estimate the test error of any model the first thing we should realize is that training error which is easy to compute but training error is not a good estimate of the test error if you remember this graph that we discussed in a previous video basically by adding flexibility to a model by adding degrees of freedom to a model we can reduce the training error as much as we like but that is not the case with the test error we saw that if we keep on increasing the flexibility of a model if we keep on adding degrees of freedom there is a point which is more or less here beyond which the