In this episode, we look at a study about whether ride-sharing services contribute to increased or decreased traffic congestion in San Francisco. We then discuss some strategies to build interpretable machine learning models.

Sources

• Science Advances article: Do transportation network companies decrease or increase congestion?

• SF-CHAMP Modeling

• KDNuggets.com: Will GDPR Make Machine Learning Illegal?

• Interpretable Machine Learning free online textbook

• Reddit.com: [P] Lime: Explaining the predictions of any machine learning classifier

• UC Business Analytics R Programming Guide: Visualizing ML Models with LIME

• arXiv.org: “Why Should I Trust You?”: Explaining the Predictions of Any Classifier

• arXiv.org: A Unified Approach to Interpreting Model Predictions