Remove 2009 Remove Predictive Modeling Remove Risk Remove Testing
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Credit Card Fraud Detection using XGBoost, SMOTE, and threshold moving

Domino Data Lab

Rules-based fraud detection (top) vs. classification decision tree-based detection (bottom): The risk scoring in the former model is calculated using policy-based, manually crafted rules and their corresponding weights. This is to prevent any information leakage into our test set. 2f%% of the test set." Hall, and W.

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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

Model distillation – this approach builds a separate explainable model that mimics the input-output behaviour of the deep network. Because this separate model is essentially a white-box, it can be used for extraction of rules that explain the decisions behind the ANN. Creating a PDP for our model is fairly straightforward.

Modeling 139
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Data Science at The New York Times

Domino Data Lab

Diving into examples of building and deploying ML models at The New York Times including the descriptive topic modeling-oriented Readerscope (audience insights engine), a prediction model regarding who was likely to subscribe/cancel their subscription, as well as prescriptive example via recommendations of highly curated editorial content.