Editing Overfitting (section)

===Remedy===
The optimal function usually needs verification on bigger or completely new datasets.  There are, however, methods like [[minimum spanning tree]] or [[life-time of correlation]] that applies the dependence between correlation coefficients and time-series (window width). Whenever the window width is big enough, the correlation coefficients are stable and don't depend on the window width size anymore. Therefore, a correlation matrix can be created by calculating a coefficient of correlation between investigated variables. This matrix can be represented topologically as a complex network where direct and indirect influences between variables are visualized. 

Dropout regularisation (random removal of training set data) can also improve robustness and therefore reduce over-fitting by probabilistically removing inputs to a layer.