LendingClub (B): Decision Trees & Random Forests
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Publication Date:
August 22, 2018
Source:
Harvard Business School
This case builds directly on the case LendingClub (A). In this case students follow Emily Figel as she builds two tree-based models using historical LendingClub data to predict, with some probability, whether borrower will repay or default on his loan. Technical topics include: (1) Decision trees as a modelling technique, overfitting and induction bias, model validation; (2) Random forest as an ensemble-style modelling technique, bootstrapping, random feature selection; and (3) Log loss as a metric for evaluating and comparing models, feature impact.
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LendingClub (B): Decision Trees & Random Forests
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