The Bellkor solution to the Netflix Prize problem involved many advanced. The inflexible method would predict E whereas the flexible method would be all over the place. Machine Learning and Data Science An Introduction to Statistical Learning. a more flexible method would do worse or better?", I take it that by "better" you mean predicting y from new observations of x, given that y is generated by the same process. Attributing all variation to x is overfitting. Whereas in reality y is just very noisy without much structure. But your flexible model will attribute all variation in y to variation in X. We know every point y is mostly determined by your disturbance term e, since its value is probably much more extreme than the value of x. This model will fit every x to every y in your sample exactly. Now imagine the opposite, you estimate an extremely flexible model y=f(X,B)+e. The Elements of Statistical Learning is a popular book on data mining and machine learning written by three statistics professors at Stanford. You just 'guess' the mean of y every time. The concepts of machine and statistical learning are introduced. A very inflexible model would be y=constant=E, this would not even take x into account. Machine Learning 6 Introduction: Explanation & Prediction FOR ANY PARTICULAR ANALYSIS. Lets say you have some data generating process y=x + e with e a stochastic disturbance term with some distribution with a really high variance and x some number between 0 and 10.
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