Naïve Bayes By professor Dr. Scott.

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Presentation transcript:

Naïve Bayes By professor Dr. Scott

Classification There exist 2 sets: Mapping according to the rule of For any , there is only one in which

Observe Symptom to identify the diseases Doctor clinic Observe Symptom to identify the diseases - fever - sneeze - cough cold Example

Naïve Bayes Classifier 。

Assume this picture is 4K clear Assuming each x is independent to each other

Gaussian distribution Multinomial: discrete value Bernoulli: binomial value

Laplace Correction P(a|y)=0 Add 1 instance in that class

Example Person height (feet) weight (lbs) foot size(inches) male 6 180 12 5.92 (5'11") 190 11 5.58 (5'7") 170 165 10 female 5 100 5.5 (5'6") 150 8 5.42 (5'5") 130 7 5.75 (5'9") 9

Person mean (height) variance (height) mean (weight) variance (weight) mean (foot size) variance (foot size) male 5.855 3.5033*10−2 176.25 1.2292*102 11.25 9.1667*10−1 female 5.4175 9.7225*10−2 132.5 5.5833*102 7.5 1.6667 Person height (feet) weight (lbs) foot size(inches) sample 6 130 8

Predict result: female

Python code

Application and problem 1. Real time prediction 2.Multi-class prediction 3. Word prediction (Spam-email classifier) 4.Recommendation system Problem: Pre-requisite: All feature independent to each other Accuracy not good enough Boosting, Bagging, ensembling won’ help improving result Feature selection and pre-processing is needed

Reference: https://www.analyticsvidhya.com/blog/2017/09/naive-bayes- explained/ http://www.cnblogs.com/leoo2sk/archive/2010/09/17/naive- bayesian-classifier.html https://en.wikipedia.org/wiki/Naive_Bayes_classifier