  We introduce a new model-agnostic explanation technique which explains the
prediction of any classifier called CLE. CLE gives an faithful and
interpretable explanation to the prediction, by approximating the model locally
using an interpretable model. We demonstrate the flexibility of CLE by
explaining different models for text, tabular and image classification, and the
fidelity of it by doing simulated user experiments.
