A method of prediction model based on random forest algorithm

摘要

#$%^&*AU2020100709A420200611.pdf#####Abstract When managing credit risk, it is a fundamental and vital segment for modem financial institutions to figure out how to effectively evaluate and identify potential default risk of borrowers before offering loans and calculate the default probability of borrowers. In this paper, the main objective of our investigation is to statistically analyze the historical loan data of banks and other financial institutions and establish a loan default prediction model by applying random forest algorithm and the thinking of unbalanced data classification. According to experiments' result, random forest algorithm performs better than decision tree and logical regression classification algorithms in predictive performance. Additionally, features highly associating with default can be obtained by prioritizing features by using random forest algorithm so that the procedure of judging the risk of offering loan is optimized.Fgre12io DeFF cision Classification 1. Sorting (classifier): md Classification KNN,- (score card) Logistic Regression Bayes Figure 12 1. #The result of RF modeled, 2. TRAIN: [ 17941 117875 67893 ... , 93992 20627 5744] TEST: [64422 113530 30105 .. , 34862 130492 127209] ' 3. the best parameter: {' X': 2, '4 ': 50} 4. the best score: 0.863112669495 ' 5. p 0.907527986443 * 6. ; 0.864487503309 e Figure 13 7