A method for explaining individual predictions in neural networks

With the proposed method, the neural network is no longer a black box. The proposed method effectively explains the predictions made by the neural network and is independent of the depth of the hidden layers and the number of nodes in each hidden layer. This provides a clear rationale for this interpretation. It can be applied to both regression and classification models. The proposed method is implemented as a Python library, making it easy to use.