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sms-spam-classification

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项目基于SMS Spam Collection数据集,构建并优化了逻辑回归模型进行垃圾短信自动分类。 采用TF-IDF方法进行特征提取,使用梯度下降算法训练模型,并通过5折交叉验证和网格搜索优化超参 数。最终,模型准确率达到0.96,精确度、召回率和F1值均表现优异,研究还通过ROC曲线和精确度 召回率曲线进一步评估了分类效果。该模型在垃圾短信分类任务中展现了较高的性能与实际实用价值

  • Updated Dec 17, 2024
  • Python

The project leverages Naive Bayes Classifiers, a family of algorithms based on Bayes’ Theorem, which presumes independence between predictive features. This theorem is crucial for calculating the likelihood of a message being spam based on various characteristics of the data.

  • Updated Jan 5, 2025
  • Jupyter Notebook

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