Convinced by the predictive power of MLP and their ability to analyze nonlinear relationships, we consider them interesting for study from their explanatory point of view. In fact, starting from input variables, MLPs have the capacity to predict the output variable but the mechanisms that occur within the network are often ignored. MLPs are often considered as black boxes. Various authors have explored this problem and proposed algorithms to illustrate the role of variables in MLP models.
Nevertheless, in most works, these methods are used to eliminate irrelevant input, and are therefore called pruning methods. First, the most significant explanatory variables are determined, then the variables which are below a fixed threshold are excluded from the network. This allows the size of the network to be reduced and thus minimizes redundancy in the training data. However, even if good prediction is required in ecology, knowing what contribution each variable makes is of prime importance. It is this explanatory aspect of MLP that we study here. These methods were used to determine the influence of each input variable and its contribution to the output. They are not, therefore, pruning methods but procedures to estimate the relative contribution of each input variable.
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