sklearn自学指南(part1)--Machine Learning in Python

最新推荐文章于 2024-10-29 04:14:31 发布
最新推荐文章于 2024-10-29 04:14:31 发布
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分类专栏： 机器学习 文章标签： 机器学习 sklearn
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本文是scikit-learn的学习笔记，介绍了这个用于预测数据分析的强大工具，它建立在NumPy、SciPy和matplotlib之上，遵循BSD协议。内容涵盖了scikit-learn的6大模块：分类、回归、聚类、降维、模型选择和预处理，适用于垃圾邮件检测、图像识别等多种应用场景。

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学习笔记，仅供参考，有错必纠
自翻译+举一反三

scikit-learn(Machine Learning in Python)

  • 预测数据分析的简单和有效的工具
  • 每个人都可以访问，并可在各种上下文中重用
  • 在NumPy、SciPy和matplotlib上构建
  • 开放源码，商业上可用，遵循BSD协议

备注：BSD开源协议是一个给于使用者很大自由的协议。可以自由的使用，修改源代码，也可以将修改后的代码作为开源或者专有软件再发布。

6大模块


  • Classification(分类)

识别一个对象属于哪个类别。


应用:垃圾邮件检测，图像识别，等。
算法:支持向量机，最近邻，随机森林，等。


  • Regression(回归)

预测与对象关联的连续值属性。


应用:药物反应，股票价格，等。
算法:SVR，最近邻，随机森林，等。


  • Clustering(聚类)
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