Essentially the tool seems to remove e.g. individual tokens from the input to a text-based classifier in order to determine their significance for the predicted

Essentially the tool seems to remove e.g. individual tokens from the input to a text-based classifier in order to determine their significance for the predicted class. From the documentation: > In order to figure out what parts of the interpretable input are contributing to the prediction, we perturb the input around its neighborhood and see how the model's predictions behave. We then weight these perturbed data points by their proximity to the original example, and learn an interpretable model on those and the associated predictions. For example, if we are trying to explain the prediction for the sentence "I hate this movie", we will perturb the sentence and get predictions on sentences such as "I hate movie", "I this movie", "I movie", "I hate", etc.