while the simplest approach to having models that are interpretable is to use algorithms that lead to interpretable models like decision treeslogistic regression and others but we don' have the guarantee that an interpretable model will provide us with the best performance hence we cannot always resort to such models recentmuch better approach is to explain model predictions in an easy-to-interpret manner by learning an interpretable model locally around the prediction this topic in fact has gained extensive attention very recently in refer to the original research paper by ribeiros singh guestrin titled "why should trust you?"explaining the predictions of any classifier from pdf/ pdf to understand more about model interpretation and the lime frameworkwhich proposes to solve this the lime framework attempts to successfully explain any black box model locally (somewhere we need define the scope of interpretation globally and locallyand you can check out the github repository at we will be leveraging another library named skateran open sourced python library designed to demystify the inner workings of of predictive models skater defines the scope of interpretating models globally(on the basis of complete datasetand locally (on the basis of an individual predictionfor global explanationsskater makes use of model-agnostic variable importance and partial dependence plots to judge the bias of model and understand its general behavior to validate model' decision policies for single predictionon the other handthe library currently embraces novel technique called local interpretable model agnostic explanation (limeribeiro et al )which uses local surrogate models to assess performance the library is authored by aaron kramerpramit choudharyand the datascience com teamskater is now mainstream project and an excellent framework for model interpretation we would like to acknowledge and thank the folks at datascience com--ian swansonpramit choudharyand aaron kramer--for developing this amazing framework and especially pramit for taking out time to explain to us in detail the features and vision for the skater project some advantages of leveraging skater are mentioned as follows and some of them are still actively being worked on and improved production ready code using functional style programming (declarative programming paradigmenable interpretation for both classification and regression based models for supervised learning problems to start with and then gradually extend it to support interpretation for unsupervised learning problems as well this includes computationally efficient partial dependence plots and model independent feature importance plots workflow abstractioncommon interface to perform local interpretation for in-memory (model is under developmentas well as deployed model (model has been deployed in productionextending lime added support for interpreting regression based modelbetter sampling distribution for generating samples around local predictionresearching the ability to include non-linear models for local evaluation enabling support of rule based interpretable models letham et al (better support for model evaluation for nlp based models bach et al layerwise relevance propagation (article?id= /journal pone better support for image interpretability batra et al gradient weighted class activation map (