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  • August 2018 (Revised September 2018)
  • Supplement
  • HBS Case Collection

LendingClub (C): Gradient Boosting & Payoff Matrix

By: Srikant M. Datar and Caitlin N. Bowler
  • Format:Print
  • | Language:English
  • | Pages:15
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Abstract

This case builds directly on the LendingClub (A) and (B) cases. In this case students follow Emily Figel as she builds an even more sophisticated model using the gradient boosted tree method to predict, with some probability, whether a borrower would repay or default on his loan.
Having now built three models, Figel compares them to determine which model is most effective at classifying borrowers correctly then uses that model to determine how to invest in a portfolio of loans. Students explore the relationship between her model’s confusion matrix, which organizes the model’s correct and incorrect classifications, the cutoff point on the curve that matches true positives and true negatives, and the payoff matrix Figel constructs. Students can then follow the link directly from the model Figel builds to her specific investment decisions.
Technical topics include: (1) Gradient boosted trees as a modelling technique; hyperparameters and learning rate; model validation; and (2) Evaluating model output; ROC curve, cutoff point, confusion matrix; payoff matrix as a framework for utilizing the model to compare investment opportunities.

Keywords

Data Analytics; Data Science; Investment; Financing and Loans; Analytics and Data Science; Analysis; Forecasting and Prediction

Citation

Datar, Srikant M., and Caitlin N. Bowler. "LendingClub (C): Gradient Boosting & Payoff Matrix." Harvard Business School Supplement 119-022, August 2018. (Revised September 2018.)
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About The Author

Srikant M. Datar

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Related Work

    • August 2018 (Revised September 2018)
    • Faculty Research

    LendingClub (C): Gradient Boosting & Payoff Matrix

    By: Srikant M. Datar and Caitlin N. Bowler
Related Work
  • LendingClub (C): Gradient Boosting & Payoff Matrix By: Srikant M. Datar and Caitlin N. Bowler
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