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出版时间:2022-11

出版社:电子工业出版社

以下为《临床大数据分析与挖掘——基于Python和机器学习的临床决策》的配套数字资源,这些资源在您购买图书后将免费附送给您:
  • 电子工业出版社
  • 9787121400391
  • 1-4
  • 403060
  • 47245498-2
  • 平塑
  • 16开
  • 2022-11
  • 429
  • 268
  • 工学
  • 计算机科学与技术
  • 计算机科学与技术
  • 本科 研究生(硕士、EMBA、MBA、MPA、博士)
目录
第1 章机器学习 ··············································································································1 1.1 机器学习简介·······································································································1 1.1.1 机器学习的概念······························································································1 1.1.2 机器学习的应用领域························································································1 1.2 机器学习通用流程································································································2 1.2.1 目标分析·······································································································2 1.2.2 数据准备·······································································································3 1.2.3 特征工程·······································································································4 1.2.4 模型训练与调优······························································································5 1.2.5 性能度量与模型应用························································································6 1.3 Python 机器学习工具库简介·················································································6 1.3.1 数据准备相关工具库························································································6 1.3.2 数据可视化相关工具库·····················································································7 1.3.3 模型训练与评估相关工具库···············································································8 小结····························································································································9 课后习题 ··················································································································· 10 第 2 章数据准备 ············································································································ 12 2.1 数据质量校验····································································································· 12 2.1.1 一致性校验·································································································· 12 2.1.2 缺失值校验·································································································· 15 2.1.3 异常值校验·································································································· 17 2.2 数据分布与趋势探查·························································································· 18 2.2.1 分布分析····································································································· 18 2.2.2 对比分析····································································································· 22 2.2.3 描述性统计分析···························································································· 25 2.2.4 周期性分析·································································································· 28 2.2.5 贡献度分析·································································································· 29 2.2.6 相关性分析·································································································· 31 VIII 2.3 数据清洗············································································································ 35 2.3.1 缺失值处理·································································································· 35 2.3.2 异常值处理·································································································· 38 2.4 数据合并············································································································ 39 2.4.1 数据堆叠····································································································· 39 2.4.2 主键合并·························································