Bayes’ Theorem provides a way that we can calculate the probability of a piece of data belonging to a given class.
Bayes’ Theorem is stated as:
P(class|data) = (P(data|class) * P(class)) / P(data)
Where P(class|data) is the probability of class given the provided data.
Naive Bayes is a classification algorithm for binary (two-class) and multiclass classification problems. It is called Naive Bayes or idiot Bayes because the calculations of the probabilities for each class are simplified to make their calculations tractable.
Naive Bayes model is easy to build and particularly useful for very large data sets. Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods.
Implementation code:
from sklearn.naive_bayes import GaussianNB
classifier = GaussianNB()
classifier.fit(X_train, y_train)
Step by step working:
Step 1: Convert the data set into a frequency table
Step 2: Create a Likelihood table by finding the probabilities like Overcast probability = 0.29 and probability of playing is 0.64.
Step 3: Now, use the Naive Bayesian equation to calculate the posterior probability for each class. The class with the highest posterior probability is the outcome of prediction.
Applications of Naive Bayes Algorithm:
Categorizing news, email spam detection, face recognition, sentiment analysis, medical diagnosis, digit recognition and weather prediction are just few of the popular applications of Naive Bayes algorithm