It is important to understand the different machine learning algorithm. We have already discussed K Nearest Neighbours classification algorithm (if you want to know about K Nearest Neighbours classification algorithm, please Click Here)
We will discuss Naïve Bayes
Based on the famous Bayes Theorem this algorithm is designed to calculate the conditional probability of an object with a feature vector which belongs to a particular class. The algorithm assumes independent occurrence of features and thus the name “Naïve”. The basic principle of Bayes theorem is used to understand the conditional probability of occurrence of features or events considering the independence of the paradigm of the features.
If you are interested in how to apply the Naïve Bayes algorithm and the mathematical intuition behind it, you can express your interest in joining the data science boot camp for free by email to
