In this section, we will delve into the Naive Bayes algorithm and build a sentiment analyzer. Naive Bayes is a popular ML algorithm based on the Bayes' theorem. The Bayes' theorem can be represented as follows:
Here, A, B are events:
- P(A|B) is the probability of A given B, while P(B|A) is the probability of B given A.
- P(A) is the independent probability of A, while P(B) is the independent probability of B.
Let's say we have the following fictitious dataset containing information about applications to Ivy League schools. The independent variables in the dataset are the applicant's SAT score, applicant's GPA, and information regarding whether the applicant's parents are alumni to an Ivy League school. The dependent variable is the outcome of the application. Based on this data, we are interested in calculating the likelihood of an applicant getting admission to an Ivy League school given that their SAT score is greater than 1...
