Bayes rule and naive bayes classifier
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Naive Bayes classifier is a simple supervised machine learning approach that can be used for classification tasks. In this presentation, you can learn about this approach and why it is called "Naive" which is one of the common interview questions.
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MLecture 1 Introduction to AI . The basics.pptx
MLecture 1 Introduction to AI . The basics.pptxFaizaKhan720183
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Machine learning and artificial intelligence introduction. Machine learning and artificial intelligence introduction.Machine learning and artificial intelligence introduction.Machine learning and artificial intelligence introduction.Machine learning and artificial intelligence introduction.Machine learning and artificial intelligence introduction.Machine learning and artificial intelligence introduction.Machine learning and artificial intelligence introduction.Bayes rule and naive bayes classifier
- 1. Ali Madani https://www.linkedin.com/in/amlearning / Bayes rule and Naive Bayes Classifier
- 2. What we know when training a model 2 p(X1=x1|Class=1) Class=1 Class=2 p(X2=x2|Class=1) p(Xm=xm|Class=1)
- 3. What do we care about? 3 p(Class=1|X1=x1,X2=x2,…,Xm=xm)=? Class=1 Class=2 ?
- 4. Bayes rule is useful to figure out the relationship 4 p(A|B)p(B)=p(B|A)p(A)
- 5. Bayes rule is useful to figure out the relationship 5 p(Class=1|X1=x1,X2=x2,…,Xm=xm)* p(X1=x1,X2=x2,…,Xm=xm)= p(X1=x1,X2=x2,…,Xm=xm|Class=1)*p(Class=1) p(A|B)p(B)=p(B|A)p(A)
- 6. The relationship looks complicated 6 WWW*p(X1=x1,X2=x2,…,Xm=xm)= p(X1=x1,X2=x2,…,Xm=xm|Class=1)p(Class=1) WWW: What We Want p(Class=1):easy to calculate
- 7. Naive assumption 7 Naive: Independent contributions of features in classification p(X1=x1,X2=x2,…,Xm=xm)=p(X1=x1)p(X2=x2)...p(Xm=xm) p(X1=x1,X2=x2,…,Xm=xm|Class=1)= p(X1=x1|Class=1)p(X2=x2|Class=1)...p(Xm=xm|Class=1)
- 9. Please share what you learned with others