Integrated Gradients
Integrated Gradients is a technique used to explain the predictions of a deep learning model by attributing importance scores to each input feature. It is based on the idea of integrating the gradients of the model's output with respect to the input features along a path from a baseline input to the actual input. The baseline input is usually a reference input with zero or neutral values for all features. By computing the integrated gradients for each feature, we can understand how much each feature contributes to the model's prediction. Integrated Gradients is a model-agnostic method and can be applied to any deep learning model, including convolutional neural networks and recurrent neural networks.
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