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.

【書誌情報】

ページ数：18ページ

サイズ：A4

商品番号：HBSP-119021

発行日：2018/8/22

登録日：2018/12/5