the general workflow within the skater package is to create an interpretation objectcreate model objectand run interpretation algorithms alsoan interpretation object takes as inputa datasetand optionally some metadata like feature names and row identifiers internallythe interpretation object will generate datamanager to handle data requests and sampling while we can interpret any model by leveraging the model estimator objectsfor ensuring consistency and proper functionality across all of skater' interfacesmodel objects need to be wrapped in skater' model objectwhich can either be an inmemorymodel object over an actual model or even deployedmodel object to take model behind an api or web service figure - depicts standard machine learning workflow and how skater can be leveraged for interpreting the two different types of models we just mentioned let' use our logistic regression model from earlier to do some model interpretation on our breast cancer datasetfigure - model interpretation in standard machine learning workflow (sourcedatascience commodel interpretation in action we will be using our train and test datasets from the breast cancer dataset that we have been using in this for consistency we will leverage the x_train and x_test variables and also the logistic model object (logistic regression modelthat we created previously we will try to run some model interpretations on this model object the standard workflow for model interpretation is to create skater interpretation and model object from skater core explanations import interpretation from skater model import inmemorymodel interpreter interpretation(x_testfeature_names=data feature_namesmodel inmemorymodel(logistic predict_probaexamples=x_traintarget_names=logistic classes_