Application of Adaboost Algorithm in Enterprise Financial Risk Analysis Model

Adaboost (Adaptive Boosting) algorithm is an ensemble learning method, which combines multiple weak classifiers to build a strong classifier, and improves the performance of the model iteratively. It is widely used in various fields, including enterprise financial risk analysis models. Therefore, this article used the Adaboost algorithm to study the financial risk analysis model, with the aim of improving the accuracy of risk prediction. This paper mainly used the method of experiment and algorithm comparison to compare the performance of Adaboost algorithm, decision tree algorithm and Logistic regression model. The experimental data showed that the recall rate of high-risk categories was 0.91, which means that the success rate of the AdaBoost model in predicting high-risk companies was 91%; the specificity was 0.902, which was 90.2% of the predicted risk-free enterprises in the model. The F1 value reached 0.818, indicating that the model performs well in predicting risk categories.