Day #34 - What is diffference between Logistics Regression and Naive Bayes

Both are probabilistic
Logistics

• Discriminative (Entire approach is purely discriminative)
• P(Y/X)
• Final Value lies between Zero and 1
• Formula given by exp(w0+w1x)/(exp(w0+ w1x)+1)
• Further can be expressed as 1/(1+(exp-(w0+ w1x))

Binary Logistic Regression - 2 class

Multinomial Logistic Regression - More than 2 class

Example - Link

Link - Ref
Logistic Regression

• Classification Model
• Probability of success as a sigmoid function of a linear combination of features
• y belongs to (0,1) - 2 Class problem
• p(yi) = 1 / 1+e-(w1x1+w2x2)
• Linear combination of features - w1x1+w2x2
• w can be found with max likelihood estimate- 

Naive Bayes

• Generative Model
• P(X/ Given Y) is Naive Bayes Assumption
• Distribution for each class

Happy Learning