摘 要

机器学习，作为一门与统计学，数学，计算机科学相关的学科，其涉及的研究领域相当广泛，其中的研究模型也数不胜数。在当今社会，机器学习已然成为科学界最重要的学科之一，人们通过机器学习中的各类模型，能够解决大量社会生产，科技创新，乃至日常生活中的实际问题。实际上，在人类每日的工作与学习中，不乏遇到各类琐碎而又复杂之问题，比如需要对一批数据按一定的特征进行合理的分类，为公司的成员在某些决策问题上做出最合适的选择等。这些问题看似比较烧脑，其实都能被归为机器学习中模型的运用层面。本文旨在通过运用三个机器学习中的模型，来分别解决两个实际问题。          

目   录

 

 

1.引言·········································5

 

2.机器学习模型·····································5

2.1.逻辑回归模型···································5

2.2.系统聚类模型···································6

2.3.k均值聚类模型·································· 7

 

3.机器学习模型的运用··································8

3.1.逻辑回归在公司决策上的运用····························8

3.1.1.研究问题···································8

3.1.2.研究数据···································8

3.1.3.逻辑回归模型的具体运用····························8

3.1.4.相关R程序语言算法······························9

3.1.5.落实具体的解决方案·······························10

3.1.6.研究案例总结及延伸·······························10

3.2.聚类分析在班级学生分数中的运用···························11

3.2.1.研究问题····································11

3.2.2.研究数据····································11

3.2.3.系统聚类的具体运用·······························11

3.2.4.相关R程序语言算法·······························11

3.2.5.不足与改进···································12

3.2.6.相关R程序语言算法·······························12

3.2.7.对数据进行分组及分析······························13

3.2.8.研究案例总结与反思·······························14

 

4.参考文献········································15

 

5.致谢··········································16

 

6.附录··········································17