One of the very famous classification problems in Machine Learning is the IRIS Flower classification problem. Iris is the family in the flower which contains several species such as the Iris setosa, Iris versicolor, Iris virginica, etc.
In this tutorial you are going to Classify the three species of Iris family based on petal width, petal length, sepal length, and sepal width.
Naïve Bayes is one of the fast and easy ML algorithms to predict a class of datasets. It can be used for Binary as well as Multi-class Classifications. It performs well in Multi-class predictions as compared to the other Algorithms. It is the most popular choice for text classification problems.
There are three types of Naive Bayes Model, which are given below:
The Gaussian model assumes that features follow a normal distribution. This means if predictors take continuous values instead of discrete, then the model assumes that these values are sampled from the Gaussian distribution. Multinomial: The Multinomial Naïve Bayes classifier is used when the data is multinomial distributed. It is primarily used for document classification problems, it means a particular document belongs to which category such as Sports, Politics, education, etc. The classifier uses the frequency of words for the predictors. Bernoulli: The Bernoulli classifier works similar to the Multinomial classifier, but the predictor variables are the independent Booleans variables. Such as if a particular word is present or not in a document. This model is also famous for document classification tasks.