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Based on a CMSTalk conversation there are several methods for feature importance we may wish to add.
Methods:
Taylor expansion (usually up to second order) of the NN output function w.r.t. the input features. Can be done during (learn when a feature becomes important) or after training (learn the most important features). Already discussed during a journal club. This presentation contains a Jupyter example. The method is described in this paper.
The shap module can be used to obtain a ranking of the input features. The method is based on the Shapley values, which originally come from game theory and are computed by integrating-out sets of input features. This is somewhat similar to the "recursive feature elimination" which is already mentioned in the documentation.
The text was updated successfully, but these errors were encountered:
Based on a CMSTalk conversation there are several methods for feature importance we may wish to add.
Methods:
Tools section:
The text was updated successfully, but these errors were encountered: