Naive Bayes is a linear classifier using Bayes Theorem and strong independence condition among features. Given a data set with n features represented by Naive Bayes states the probability of output: Y from features F_i is, Bayes theorem states that: Logistic regression is a linear classification method that learns the probability…
Naive Bayes is a very popular Supervised Classification algorithm. This algorithm is called “Naive” because it makes a naive assumption that each feature is independent of other features. It is near to impossible to find such data sets in real life. Bayes’ theorem is the base for Naive Bayes Algorithm….
Logistic Regression is a supervised classification algorithm that is used to predict the probability of a categorical dependent variable using a given set of independent variables. It is a predictive analysis algorithm and based on the concept of probability. The most common use of logistic regression models is in binary classification problems. Some…