本研究使用傳統的Logistic回歸模型與機器學習的隨機森林模型對P2P借貸的個人信用風險進行評估預測。本研究的數據來源於LendingClub的2018年度公開數據資料，先對P2P借貸的個人信用風險因素進行挑選，再使用挑選出的變量對Logistic回歸模型與隨機森林模型進行訓練，並用測試集檢驗兩個模型對個人信用風險的預測能力。結果表明，隨機森林模型的在決策樹為800棵，每棵決策樹的特征值為3個的時候，隨機森林模型預測準確率最高。與Logistic回歸模型比較，隨機森林模型有著更高的精度。本研究還對兩個模型進行了模型性能的比較，結果表明隨機森林的模型性能好過去Logistic回歸模型。

In this paper we evaluate and predict the private credit risk in P2P lending by using traditional Logistic regression and Random Forest in machine learning. For the open data from LendingClub in 2018, we select the private credit risk factors of P2P lending first, and train the Logistic regression and Random Forest model with selected variables. We test the prediction ability of two models with test set. The result shows that when there are 800 decision trees and 3 features for each tree in Random Forest model, accuracy of the model reaches best. Compared with Logistic regression, Random Forest has higher precision. We also compare the performance of two models, which shows that Random Forest is better than Logistic regression.

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