API World + AI DevWorld

Complex machine learning models are frequently termed “black boxes”. While these models deliver highly accurate predictions, they frequently provide no insight into the factors driving the predictions. In turn, this can make it problematic to ensure that the model’s predictions are actionable. For instance, in churn prediction, explaining why the customer is predicted to churn is as important as the prediction itself, as it enables action to be taken to address highlighted issues and prevent the churn.In recent years, as operationalized ML solutions become ever more complex and prevalent in every facet of life, the interpretability of models has become a critical consideration. In the last couple of years, an increasing number of papers and open source software libraries have appeared to help data scientists explain their predictions. In this presentation, we discuss recent approaches to interpreting the results from complex machine learning models, including the well-known LIME package. We present best practices for their use and deployment and provide cautionary examples and caveats associated with leveraging these techniques.