<ul><li>The importance of features in a neural network is determined by how much the output changes when a feature is changed by 1 unit.</li><li>The process of calculating feature importance is similar to how parameters are adjusted in a neural network using partial derivatives and the chain rule.</li><li>To compute feature importance, all possible routes from the input to the output are considered, and the weights and derivatives of activation functions along these routes are multiplied.</li><li>The resulting multiplications for each input are summed to determine the overall feature importance.</li></ul>

Feature importance in Neural Network

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