Bayes and naive bayes
Download as PPTX, PDF
0 likes32 views
Naive Bayes is a classification technique that uses Bayes' theorem with an assumption of independence between predictors. It calculates the probability of class membership as the product of the prior probability of the class and the likelihoods of the predictor values given the class. The probabilities stored for a naive Bayes model include the class probabilities, calculated from the frequency of each class, and the conditional probabilities of each predictor value given the class, calculated from frequency counts. Naive Bayes classification involves determining the class with the highest posterior probability given the predictor values.
Bayes and naive bayes
- 1. Bayes and Na誰ve Bayes 3/31/2020 Shivani Saluja 1
- 6. Types of Probability The representation for naive Bayes is probabilities. A list of probabilities are stored to file for a learned naive Bayes model. This includes: Class Probabilities: The probabilities of each class in the training dataset. Conditional Probabilities: The conditional probabilities of each input value given each class value. 3/31/2020 Shivani Saluja 6
- 7. Na誰ve Bayes It is a classification technique based on Bayes Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter. Even if these features depend on each other or upon the existence of the other features, all of these properties independently contribute to the probability that this fruit is an apple and that is why it is known as Naive. Naive Bayes model is easy to build and particularly useful for very large data sets 3/31/2020 Shivani Saluja 7
- 8. Na誰ve Bayes P(c|x) is the posterior probability of class (c, target) given predictor (x, attributes). P(c) is the prior probability of class. P(x|c) is the likelihood which is the probability of predictor given class. P(x) is the prior probability of predictor. 3/31/2020 Shivani Saluja 8
- 9. Calculating Class Probabilities The class probabilities are simply the frequency of instances that belong to each class divided by the total number of instances. For example in a binary classification the probability of an instance belonging to class 1 would be calculated as: P(class=1) = count(class=1) / (count(class=0) + count(class=1)) In the simplest case each class would have the probability of 0.5 or 50% for a binary classification problem with the same number of instances in each class. 3/31/2020 Shivani Saluja 9
- 10. Conditional Probability The conditional probabilities are the frequency of each attribute value for a given class value divided by the frequency of instances with that class value. For example, if a weather attribute had the values sunny and rainy and the class attribute had the class values go-out and stay-home, then the conditional probabilities of each weather value for each class value could be calculated as: P(weather=sunny|class=go-out) = count(instances with weather=sunny and class=go-out) / count(instances with class=go-out) P(weather=sunny|class=stay-home) = count(instances with weather=sunny and class=stay-home) / count(instances with class=stay-home) P(weather=rainy|class=go-out) = count(instances with weather=rainy and class=go-out) / count(instances with class=go-out) P(weather=rainy|class=stay-home) = count(instances with weather=rainy and class=stay-home) / count(instances with class=stay-home) 3/31/2020 Shivani Saluja 10
- 11. Na誰ve Bayes 3/31/2020 Shivani Saluja 11
- 12. 3/31/2020 Shivani Saluja 12