besides thissincethe time this project startedthey have committed some improvementsnamely support for regression back into the original lime repository and they still have other aspects of interpretation in their roadmap and further improvements to lime in the future you can easily install skater by running the pip install - skater command from your prompt or terminal for more informationyou can check out the github repository at join the chat group hereunderstanding skater skater is an open source python framework that aims to provide model agnostic interpretation of predictive models it is an active project on github at the previously mentioned features being worked upon actively the idea of skater is to understand black box machine learning models by querying them and interpreting their learned decision policies the philosophy of skater is that all models should be evaluated as black boxes and decision criteria of the models are inferred and interpreted based on input perturbations and observing the corresponding output predictions the scope of model interpretation by leveraging skaterenables us to do both global and local interpretation as depicted in figure - figure - scope of model interpretation (sourcedatascience comusing the skater librarywe can explore the featuresimportancepartial dependency plots upon featuresand global and local fidelity of the predictions made by the model the fidelity of model can be described as the reasons on the basis of which the model calculated and predicted particular class for examplesuppose we have model that predicts whether particular user transaction can be tagged as fraudulent transaction or not the output of the model will be much more trustworthy if we can identifyinterpretand depict that the reason the model marked the prediction as fraudis because the amount is larger than the maximum transaction of the user in the last six months and the location of transaction is kms away from user' normal transaction areas contrast it with the case where we are only given prediction label without any justifying explanation