预测消费者LPG补充频率:一项使用可解释机器学习的研究
Shrawan Kumar Trivedi1, Abhijit Deb Roy2, Praveen Kumar3
1Business Analytics and Information Systems Area, Rajiv Gandhi Institute of Petroleum Technology, Amethi, India.
Heliyon
|January 11, 2024
概括
总理乌日瓦拉计划 (PMUY) 面临着LPG补充的挑战. 一个可解释的人工智能模型准确地预测受益人的补充频率,改善目标和计划的可持续性.
科学领域:
- 数据科学数据科学数据科学
- 公共政策 公共政策
- 机器学习 机器学习
背景情况:
- 总理乌贾瓦拉计划 (PMUY) 旨在为印度农村妇女提供LPG连接,但在持续补充采用方面面临挑战.
- 这影响了该计划的可持续性和对受益者的有效准.
研究的目的:
- 使用可解释机器学习 (XAI) 开发受益者LPG补充频率的预测模型.
- 加强PMUY计划的目标策略和可持续性.
主要方法:
- 提出了一个增强的堆叠支持向量机 (SVM) 模型 (ISS),并与随机森林,SVM-RBF,天真贝叶斯和决策树模型进行了比较.
- 使用准确度,灵敏度,特异性,科恩卡帕,ROC和AUC进行性能评估,并进行了10倍的交叉验证.
- 使用可解释AI (XAI) 技术来理解特征的重要性和模型相互作用.
主要成果:
- 拟议的ISS模型在各种数据分割 (50-50,66-34,80-20) 中实现了最佳的整体准确性.
- XAI模型提供了对特征贡献的见解,有助于理解预测驱动因素.
- 该研究证明了XAI在分析政策干预的用户行为方面的有效性.
结论:
- 开发的XAI驱动的预测模型可以有效地预测PMUY受益人中的LPG补充频率.
- 这种方法为优化受益人准和为计划可持续性提供政策干预提供了有价值的工具.
- 这些发现支持在公共政策中使用先进的机器学习和XAI,以改善社会计划成果.
更多相关视频
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
8.3K
08:45Modeling Alcohol Consumption in Rodents Using Two-Bottle Choice Home Cage Drinking and Microstructural Analysis
Published on: November 8, 2024
599
相关概念视频
01:07Residual 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
01:25Multiple Regression
3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
43
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
43
01:11Residuals and Least-Squares Property
7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K
01:03Prediction 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
01:11Observational Studies
8.6K
Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
8.6K