【无标题】

机器学习第五次作业

6.1

由第一次检测:
P(⊕∣cancer)P(cancer)=0.0078P(⊕∣cancer)P(cancer)=0.0078P(⊕∣cancer)P(cancer)=0.0078
P(⊕∣¬cancer)P(¬cancer)=0.0298P(⊕∣\neg cancer)P(\neg cancer)=0.0298P(⊕∣¬cancer)P(¬cancer)=0.0298、

将第一次检测结果作为新的先验概率,归一化得到:
P(cancer∣⊕)=0.0078/0.0376=0.2074P(cancer∣⊕)=0.0078/0.0376=0.2074P(cancer∣⊕)=0.0078/0.0376=0.2074
P(¬cancer∣⊕)=0.0298/0.0376=0.7926P(\neg cancer∣⊕)=0.0298/0.0376=0.7926P(¬cancer∣⊕)=0.0298/0.0376=0.7926

第二次检测的后验概率:
P(cancer∣⊕,⊕)=P(⊕∣cancer)∗P(cancer∣⊕)=.098∗0.2074=0.2033P(cancer∣⊕,⊕)=P(⊕∣cancer)*P(cancer∣⊕)=.098*0.2074=0.2033P(cancer∣⊕,⊕)=P(⊕∣cancer)∗P(cancer∣⊕)=.098∗0.2074=0.2033
P(¬cancer∣⊕,⊕)=P(⊕∣¬cancer)∗P(¬cancer∣⊕)=0.03∗0.7926=0.0238P(\neg cancer∣⊕,⊕)=P(⊕∣\neg cancer)*P(\neg cancer∣⊕)=0.03*0.7926=0.0238P(¬cancer∣⊕,⊕)=P(⊕∣¬cancer)∗P(¬cancer∣⊕)=0.03∗0.7926=0.0238

归一化得:
P(cancer∣⊕,⊕)=0.2033/0.2271=0.8952P(cancer∣⊕,⊕)=0.2033/0.2271=0.8952P(cancer∣⊕,⊕)=0.2033/0.2271=0.8952
P(¬cancer∣⊕,⊕)=0.0238/0.2271=0.1048P(\neg cancer∣⊕,⊕)=0.0238/0.2271=0.1048P(¬cancer∣⊕,⊕)=0.0238/0.2271=0.1048

6.2

根据贝叶斯公式:
P(cancer∣⊕)=P(⊕∣cancer)∗P(cancer)P(⊕)=P(⊕∣cancer)∗P(cancer)P(⊕∣cancer)P(cancer)+P(⊕∣¬cancer)P(¬cancer)=1P(⊕∣cancer)P(cancer)+P(⊕∣¬cancer)P(¬cancer)P(⊕∣cancer)∗P(cancer)=11+P(⊕∣¬cancer)P(¬cancer)P(⊕∣cancer)∗P(cancer)P(cancer∣⊕)=\frac{P(⊕∣cancer)*P(cancer)}{P(⊕)}\\=\frac{P(⊕∣cancer)*P(cancer)}{P(⊕∣cancer)P(cancer)+P(⊕∣\neg cancer)P(\neg cancer)}\\=\frac{1}{\frac{P(⊕∣cancer)P(cancer)+P(⊕∣\neg cancer)P(\neg cancer)}{P(⊕∣cancer)*P(cancer)}}\\=\frac{1}{1+\frac{P(⊕∣\neg cancer)P(\neg cancer)}{P(⊕∣cancer)*P(cancer)}}P(cancer∣⊕)=P(⊕)P(⊕∣cancer)∗P(cancer)=P(⊕∣cancer)P(cancer)+P(⊕∣¬cancer)P(¬cancer)P(⊕∣cancer)∗P(cancer)=P(⊕∣cancer)∗P(cancer)P(⊕∣cancer)P(cancer)+P(⊕∣¬cancer)P(¬cancer)1=1+P(⊕∣cancer)∗P(cancer)P(⊕∣¬cancer)P(¬cancer)1

而将P(⊕∣cancer)P(cancer)P(⊕∣cancer)P(cancer)P(⊕∣cancer)P(cancer)和P(⊕∣¬cancer)P(¬cancer)P(⊕∣\neg cancer)P(\neg cancer)P(⊕∣¬cancer)P(¬cancer) 归一化,得到的正是P(⊕∣cancer)P(cancer)P(⊕)\frac{P(⊕∣cancer)P(cancer)}{P(⊕)}P(⊕)P(⊕∣cancer)P(cancer)和P(⊕∣¬cancer)P(¬cancer)P(⊕)\frac{P(⊕∣\neg cancer)P(\neg cancer)}{P(⊕)}P(⊕)P(⊕∣¬cancer)P(¬cancer),而前者正是P(cancer∣⊕)P(cancer∣⊕)P(cancer∣⊕),因此这样是正确的

相关推荐
天国梦1 小时前
AI时代营销全链路落地实战指南
大数据·人工智能
长夜多忧思1 小时前
机器学习_感知机
机器学习·感知机
数智工坊1 小时前
RealNet:用于异常检测的特征选择网络,具备真实的合成异常样本
人工智能·深度学习
阳明山水2 小时前
TimesFM与Moirai MoE零样本预测解析
人工智能·深度学习·算法·机器学习·架构
在世修行2 小时前
第24篇:PyInstaller打包实战 — 从Python脚本到Windows EXE
人工智能·python·pyinstaller
一次旅行2 小时前
AI 前沿日报 | 2026年07月14日
人工智能
智慧物业老杨2 小时前
物业单盘精细化预算数智化方案:IoT采集+AI建模全链路自动化落地体系
人工智能·物联网·自动化
铅笔侠_小龙虾2 小时前
Rust 学习(2)-变量、常量与 shadowing
学习·算法·rust
AI科技星2 小时前
基于全域数学公理体系的三元极值题最简求解法【乖乖数学】
线性代数·算法·游戏·决策树·机器学习·乖乖数学·全域数学
xn71332 小时前
别再把 MCP Roots 当安全沙箱:一个符号链接就能逃逸目录
人工智能·后端·mcp