Loading... # Data Mining - Week3, Classfication ## 3.1 Classification *Definition*: A kind of **supervised** learning ## 3.2 Bayes Theorem $$ P(A \cup B = P(A) + P(B) - P(A \cap B) \tag{3.1} $$ $$ P(A \cap B) = P(A|B)P(B) = P(B|A)P(A) \tag{3.2} $$ $$ P(A|B) = \frac{P(B|A)P(A)}{P(B)} \tag{3.3} $$ ## 3.3 Naive Bayes Classifier $$ \omega_{MAP}=\underset{\omega_i<\omega}{\arg \max}P(\omega_i)\prod_{j}P(a_j|\omega_j) \tag{3.4} $$ - MAP: Maximum A Posterior *Independent*: - $P(A \cap B) = P(A)P(B)$ *Conditionally Independent*: - $P(A,B|G)=P(A|G)P(B|G)$ - or $P(A|G, B)=P(A|G)$ > Independent $\neq$ Uncorrelated Estimating $P(a_j|\omega_i)$: **Laplace Smoothing**: $P(a_{jk}|\omega_i)=\frac{|a_j=a_{jk} \land \omega=\omega_i +1|}{|\omega=\omega_i|+|a_j|}$ ## 3.4 Decision Tree  ### 3.4.1 ID3, C4.5, CART > To: https://easyai.tech/ai-definition/decision-tree/ 最后修改：2021 年 10 月 09 日 © 允许规范转载 赞 0 如果觉得我的文章对你有用，请随意赞赏