Overfitting is a modeling error which occurs when a function is too closely fit to a set of data points, which is a no-no in data science and limits the practical usability of a model. One can, in theory, create a model that explains all the data points of a particular test data set extremely well to the point that too many parameters are used to explain away most residual variation (i.e. all the noise). The consequences of using an overfitted model is that the overfitted model becomes ill- suited to explain the behavior of another data set representing the general population, because it has been over tailored to the test data set.
