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Hui Lin

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Model Error

The model error consists of two parts: (1) systematic error, it is the part of “systematic relation” between X and y that model fails to capture. This part of the error can be improved by improving the model; (2) random error, it is the part of the response that can not be explained by current inputs. A more complex model does not reduce the error. The biggest problem with black-box models is to fit random error as well, i.e., over-fitting. The notable feature of random error is that it can not be repeated on different samples. So a way to determine whether there is overfitting is to reserve a part of the sample as a test set and then check the performance of the trained model on the test data. Note that overfitting is a general problem, and any model may be overly fitted. Because black-box model usually has a large number of parameters, it is more suspectable to over-fitting.

We will delve deeper into model error and study the impact of measurement error in response variable and independent variables.

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