The first problem is a familiar one:
overfitting. Because of a large number of parameters, the model tends to fit the sample unrealistically well but falls badly for out-of-sample forecasting.(1) To see how unbelievable the
overfitting problem could become, Chart A displays actual values and in-sample (not out-of-sample) forecasts of the stock of MI from January 1960 to March 1996.
The criterion is usually some form of goodness-of-fit function of the model to the data, perhaps tempered by a smoothing term to avoid
overfitting, or generating a model with too many degrees of freedom to be constrained by the given data.
This is the phenomena known as
overfitting. The curve-fitting problem is about minimizing the distance to Best(PAR) by making a judicious choice between L(PAR) and L(LIN) (the best-fitting member of LIN).
This problem is called
overfitting, and is familiar from other function estimation procedures.
Moreover, going beyond a simple linear form risks
overfitting our sample data, thereby distorting our results.
Because the
overfitting is one of the main drawback [28].
Otherwise, the deep neural network may be
overfitting or not robust.
However, it is susceptible to local maxima trap problem, which could result in
overfitting of the resulting model [14].