Research on Rural Credit Risk Assessment Model Based on Machine Learning Algorithms

In order to precisely manage and control rural credit risks, this study delved deeply into the assessment model predicated on machine learning algorithms. During the data collection phase, 10000 copies of farmers' credit records spanning nearly five years were procured from financial institutions such as rural credit cooperatives and agricultural banks, encompassing over 20 characteristic data items including age, income, land area, and past loan repayment situations. After data cleansing to handle missing values and outliers, the training set and test set were partitioned in a 7:3 ratio. The logical regression, decision tree, random forest, and support vector machine algorithms were employed for modeling. The logical regression optimized parameters via the gradient descent method; the decision tree selected splitting attributes based on information gain; the random forest constructed 100 decision trees and conducted comprehensive voting; the support vector machine mapped data to a high-dimensional space through kernel functions to seek the optimal classification hyperplane. It was experimentally discovered that the random forest algorithm exhibited optimal performance, with an astonishingly high accuracy rate of 85% on the test set, a recall rate of 82%, and an F1 value reaching 0.83, significantly outperforming other algorithms. This model provides robust support for rural credit decision-making by financial institutions, holds the promise of reducing the non-performing loan rate, and propels the healthy development of rural finance.