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Datamining 3rd Naivebayes
Datamining 3rd Naivebayes
Datamining 3rd Naivebayes
Datamining 3rd Naivebayes
Datamining 3rd Naivebayes
Datamining 3rd Naivebayes
Datamining 3rd Naivebayes
Datamining 3rd Naivebayes
Datamining 3rd Naivebayes
Datamining 3rd Naivebayes
Datamining 3rd Naivebayes
Datamining 3rd Naivebayes
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Datamining 3rd Naivebayes

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  • 1. 2
  • 2. 3
  • 3. 5.5.1 Theorem) n X = (T1 = x1 )∧(T2 = x2 )∧· · ·∧(Tn = xn ) CH X C = {C1 , C2 , ...} X CH X CH 5.3 P (CH ∩ X) P (X | CH )P (CH ) P (CH | X) = CH = (C = ) P (X) P (X) P (CH |A) A P (CH |X) 5.4 P (CH ∩ X) = P (CH | X)P (X) = PP (C| CHX) (CH ) 4 H ) (X H ∩ )P P (X|C
  • 4. P (CH ∩ X) P (X | CH )P (CH ) P (CH | X) = = P (X) P (X)
  • 5. P (C = | X) > P (C = × | X) P (C = | X) < P (C = × | X) 6
  • 6. P (X | C )P (C ) P (C | X) = P (X) P (C ) = N /N X = (T1 = x1 ) ∧ (T2 = x2 ) ∧ · · · ∧ (Tn = xn ) = x1 ∧ x2 ∧ · · · ∧ x3 P (X | C ) = P (x1 ∧ x2 ∧ · · · ∧ xn | C ) = P (x1 | C )P (x2 | C ) · · · P (xn | C ) n = P (xk | C ) k=1 P (X) CH
  • 7. P (C ) = 4/10 = 0.4, P (C× ) = 6/10 = 0.6 X = (T1 = No) ∧ (T2 = No) ∧ (T3 = Yes) ∧ (T4 = Yes) 8
  • 8. X = (T1 = No) ∧ (T2 = No) ∧ (T3 = Yes) ∧ (T4 = Yes) P (X | C ) = P (T1 = No | C ) × P (T2 = No | C ) ×P (T3 = Yes) | C ) × P (T4 = Yes | C ) P (T1 = No | C ) = 2/4 = 0.5 P (T2 = No | C ) = 0/4 = 0.0 P (T3 = Yes | C ) = 2/4 = 0.5 P (T4 = Yes | C ) = 0/4 = 0.0 P (X|C ) = 0.5 × 0.0 × 0.5 × 0.0 = 0.0 P (X | C ) · P (C ) = 0.0 × 0.4 = 0.0
  • 9. X = (T1 = No) ∧ (T2 = No) ∧ (T3 = Yes) ∧ (T4 = Yes) P (X | C× ) = P (T1 = No | C× ) × P (T2 = No | C× )× P (T3 = Yes | C× ) × P (T4 = Yes | C× ) P (T1 = No | C× ) = 4/6 = 0.667 P (T2 = No | C× ) = 4/6 = 0.667 P (T3 = Yes | C× ) = 1/6 = 0.167 P (T4 = Yes | C× ) = 4/6 = 0.667 P (X|C× ) = 0.667 × 0.667 × 0.167 × 0.667 = 0.0494 P (X | C× ) · P (C× ) = 0.0494 × 0.4 = 0.0198
  • 10. P (X | C ) · P (C ) = 0.0 P (X | C× ) · P (C× ) = 0.0198 P (X | C ) · P (C ) < (X | C× ) · P (C× ) 11
  • 11. 12

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