Microsoft SQL Server 2005 Data Mining Nave Bayes

Microsoft SQL Server 2005 Data Mining 演算法 – Naïve Bayes 謝邦昌輔仁大學統計資訊學系教授中華資料採礦協會理事長 stat 1001@mails. fju. edu. tw WWW. CDMS. ORG. TW 1

Naïve Bayes Classifier 單純貝氏分類 2

Bayesian Classification • Bayes Theorem: P(H|X)=P(X|H)P(H)/P(X) –Let X is a data sample whose class label is unknown –Let H be a class label –P(H) is the prior probability of H –P(H|X) is the posterior probability of H conditioned on X –Example • X: a red and round object • C: being an apple or an orange • H: being an apple (H C) 26

Naive Bayesian Classification Classify X=(age=“<=30”, income=“medium”, student=“yes”, credit-rating=“fair”) – – – – P(buys_computer=yes) = 9/14 P(buys_computer=no)=5/14 P(age=<30|buys_computer=yes)=2/9 P(age=<30|buys_computer=no)=3/5 P(income=medium|buys_computer=yes)=4/9 P(income=medium|buys_computer=no)=2/5 P(student=yes|buys_computer=yes)=6/9 P(student=yes|buys_computer=no)=1/5 P(credit-rating=fair|buys_computer=yes)=6/9 P(credit-rating =fair|buys_computer=no)=2/5 P(X|buys_computer=yes)=0. 044 P(X|buys_computer=no)=0. 019 P(buys_computer=yes|X) P(X|buys_computer=yes) P(buys_computer=yes)=0. 028 – P(buys_computer=no|X) P(X|buys_computer=no) P(buys_computer=no)=0. 007 27

Microsoft Naive Bayesian • Easy to Train Model • Algorithm Parameters – MAXIMUM_INPUT_ATTRIBUTES • 超過此值會自動啟動欄位篩選機置 – MAXIMUM_STATES • 類型屬性項目最大值 – MINIMUM_DEPENDENCY_PROBABILITY • 介於 0~1，數值越大，模型使用變數會減少 28