TD_NaiveBayes classification algorithm takes a training data set with known discrete outcomes and either discrete or continuous numeric input variables and categorical variables and generates a model that you can use to predict the outcome of future observations based on their input variable values. The model assumes that, given the outcome, the input variables are independent of each other.
The Naive Bayes algorithm is a supervised learning algorithm, which is based on the Bayes theorem and used for solving classification problems. It is a probabilistic classifier, which means it predicts on the basis of the probability of an object.