Algorithm Description 

Naïve Bayes is a probabilistic classification algorithm, based on Bayes Theorem. Bayes Theorem suggests that we can find the probability of an event, given the probability another event has occurred. With this algorithm, we must assume that each feature makes an independent and equal contribution to the analysis. This translates to the assumptions that no pair of features are dependent, and each feature equally contributes to the classification.  

Using Bayes Theorem, the Naïve Bayes Classifier formula can be written as follows:

The left hand side of the equation is called the posterior probability, and this can be thought of as the ‘probability the data is included in class y, given all features’. The class of the data point is based on the value of the greatest posterior probability.  

One disadvantage of Naïve Bayes is that if you have no occurrences of a class label and a certain attribute value together, then the probability estimate will be zero. LityxIQ uses the Laplace Correction to rectify this problem.

Additional Links

https://www.kdnuggets.com/2020/06/naive-bayes-algorithm-everything.html

https://www.analyticsvidhya.com/blog/2017/09/naive-bayes-explained/ 

 https://medium.com/x8-the-ai-community/a-simple-introduction-to-naive-bayes-23538a0395a 

https://www.geeksforgeeks.org/naive-bayes-classifiers

Lityx IQ Parameters 

Laplace Correction  - Sample Size to use to correct for zero probabilities in the estimates.

Maximum No. of Model terms - The maximum number of terms used during the variable selection process. Larger values may have a longer processing time, but smaller values may miss important variables.