关于客户流失预测和模型解释性分析的研究.
Ke Peng1, Yan Peng1, Wenguang Li1
1College of Computer Science and Engineering, Sichuan University of Science and Engineering, Yibin, China.
PloS one
|December 8, 2023
概括
本研究介绍了一种GA-XGBoost模型,用于预测和防止商业银行的客户流失. 该模型实现了高准确性,为改善客户保留策略提供了对关键流失驱动因素的洞察.
科学领域:
- 计算智能是一种计算智能.
- 金融技术 金融技术
- 机器学习应用 机器学习应用
背景情况:
- 银行业的竞争加剧和客户选择多样化导致了客户流失的增加.
- 预测客户行为和保留现有客户是商业银行面临的关键挑战.
- 由于信息和互联网技术的进步,传统的客户忠诚度正在下降.
研究的目的:
- 使用银行业务数据,为商业银行构建一个准确的客户流失预测模型.
- 通过模型解释性分析,识别影响客户流失的关键特征.
- 为银行提供可操作的决策支持,以防止客户流失和改善客户保留.
主要方法:
- 利用Kaggle平台的银行客户数据进行分析.
- 采用多种采样方法,包括SMOTEENN,SMOTE和ADASYN,用于数据平衡.
- 使用遗传算法-XGBoost (GA-XGBoost) 分类器开发了一个客户流失预测模型.
- 使用Shapley值进行解释性分析,以了解特征影响.
主要成果:
- 与SMOTE和ADASYN相比,SMOTEENN在平衡不平衡的银行数据方面表现优越.
- 该GA-XGBoost模型实现了最佳的F1和AUC分数分别为90%和99%,优于其他六种机器学习模型.
- 确定的主要流失预测因素包括交易总额,交易金额,产品数量和总销售余额.
结论:
- 该GA-XGBoost模型提供了一种有效的解决方案,用于识别和预测银行业的客户流失.
- 来自Shapley价值观的可解释见解使银行能够了解流失驱动因素并提高客户服务质量.
- 该研究为商业银行和其他行业提供了有价值的参考资料,旨在减少客户流失和减轻损失.
相关概念视频
Prediction Intervals
2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.3K
Residual Plots
4.6K
A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
When the residual values are plotted against the variable x, it is called a residual...
4.6K
Regression Analysis
5.7K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
5.7K
Variation
6.8K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
6.8K
Predicting Products: Substitution vs. Elimination
11.7K
When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
The following factors can influence the mechanisms competing against each other:
11.7K
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches
134
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
134


