Naïve Bayes Classifiers
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- Corey Norton
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1 For a ore in-depth introdction to Naïe Bayes lassifiers and the theory srronding the, please see Andrew s lectre on robability for Data Miners. Naïe Bayes lassifiers Note to other teachers and sers of these slides. Andrew wold be delighted if yo fond this sorce aterial sefl in giing yor own lectres. Feel free to se these slides erbati, or to odify the to fit yor own needs. oweroint originals are aailable. If yo ake se of a significant portion of these slides in yor own lectre, please inclde this essage, or the following link to the sorce repository of Andrew s ttorials: oents and corrections grateflly receied. opyright 2004, Andrew W. Moore Andrew W. Moore rofessor School of opter Science arnegie Mellon Uniersity aw@cs.c.ed These notes asse yo hae already et Bayesian Networks
2 A siple Bayes Net erson is a nior Broght oat to lassroo Lie in zipcode 523 Saw etrn of the King ore than once opyright 2004, Andrew W. Moore Slide 2
3 A siple Bayes Net What paraeters are stored in the Ts of this Bayes Net? erson is a nior Broght oat to lassroo Lie in zipcode 523 Saw etrn of the King ore than once opyright 2004, Andrew W. Moore Slide 3
4 A siple Bayes Net erson is a nior Broght oat to lassroo Lie in zipcode 523 Saw etrn of the King ore than once ~ ~ ~ Sppose we hae a database fro 20 people who attended a lectre. How cold we se that to estiate the ales in this T? opyright 2004, Andrew W. Moore Slide 4
5 A siple Bayes Net erson is a nior Broght oat to lassroo # people who walked to school Lie in zipcode 523 # people in database Saw etrn of the King ore than once # people who walked to school and broght a coat F # people who walked to school ~ ~ ~ # coat - bringers who didn' t walk to school # people who didn' t walk to school Sppose we hae a database fro 20 people who attended a lectre. How cold we se that to estiate the ales in this T? opyright 2004, Andrew W. Moore Slide 5
6 A Naïe Bayes lassifier Otpt Attribte Walked to School Broght oat to lassroo Lie in zipcode 523 Saw etrn of the King ore than once Inpt Attribtes ~ ~ ~ A new person shows p at class wearing an I lie right aboe the Manor Theater where I saw all the Lord of The ings Moies eery night oercoat. What is the probability that they are a nior? opyright 2004, Andrew W. Moore Slide 6
7 opyright 2004, Andrew W. Moore Slide 7 Naïe Bayes lassifier Inference + +
8 The General ase Estiate as fraction of records with 2. Estiate i as fraction of records that also hae. 3. To the ale gien obserations of all the i ales, copte argax L opyright 2004, Andrew W. Moore Slide 8
9 opyright 2004, Andrew W. Moore Slide 9 Naïe Bayes lassifier argax L n j j j argax argax L argax L L argax L L Becase of the strctre of the Bayes Net
10 More Facts Abot Naïe Bayes lassifiers Naïe Bayes lassifiers can be bilt with real-aled inpts* ather Technical oplaint: Bayes lassifiers don t try to be axially discriinatie---they erely try to honestly odel what s going on* ero probabilities are painfl for oint and Naïe. A hack jstifiable with the agic words Dirichlet rior can help*. Naïe Bayes is wonderflly cheap. And sries 0,000 attribtes cheerflly! *See ftre Andrew Lectres opyright 2004, Andrew W. Moore Slide 0
11 What yo shold know How to bild a Bayes lassifier How to with a B opyright 2004, Andrew W. Moore Slide
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