NGO-LSSVM北方苍鹰算法优化最小二乘支持向量机分类预测+特征贡献SHAP分析，通过特征贡献分析增强模型透明度，Matlab代码实现，引入SHAP方法打破黑箱限制，提供全局及局部双重解释视角

代码功能

该Matlab代码实现了一个基于NGO-LSSVM北方苍鹰算法最小二乘支持向量机分类预测模型，结合了SHAP可解释性分析。最小二乘支持向量机核函数选择RBF，北方苍鹰算法最小二乘支持向量机的c和g两个参数，主要功能包括：

算法步骤

技术路线

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运行环境要求

应用场景

注意：实际应用中需根据数据特性调整输入维度。SHAP分析部分计算成本较高，可通过减少numShapSamples参数控制样本量。

数据集

完整代码私信回复NGO-LSSVM分类预测+特征贡献SHAP分析，通过特征贡献分析增强模型透明度，Matlab代码实现