Overfitting Machine Learning Models


The third model in the picture fit better the predictor variable. It models the training data so well!  But it is not the best one! Putting it into production will produce big prediction errors over new data.



“Overfitting is a modeling error that occurs when a function is too closely fit to a limited set of data points. Overfitting the model generally takes the form of making an overly complex model to explain idiosyncrasies in the data under study “(1)  .

Machine Learning is not to compete with a formal math function. It is not a good idea to compare with formal math equations. Machine learning algorithms describe the world in some different way, not fully comprehensible, but for sure most of the time even more accurate than math models developed by scientist.




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