Introduction to the Naïve Bayes Algorithm
Greetings.
I am a machine learning engineer based in India, possessing a sustained interest in machine learning since my undergraduate studies. I have completed Stanford University's machine learning course (Andrew Ng) via Coursera, and IBM's machine learning and deep learning curriculum. My current focus is on machine learning and data science projects, aiming to leverage my expertise for impactful, real-world problem-solving.
The Naïve Bayes is a classification algorithm based on Bayes' theorem. It is said to be naïve because the foundation of this algorithm is based on naïve assumptions.
- Some of the advantages of the Naïve Bayes Algorithm are:
It is a very simple algorithm for classification problems compared to other classification algorithms.
It is also a powerful algorithm, which implies that it is faster to predict labels using it compared to other classification algorithms.
Another advantage of using it is that it can also give better results on small datasets compared to other algorithms.
Assumption of Naïve Bayes
The Naïve Bayes algorithm is a naïve assumption that implies the condition for independence, of course.
Simply put, one cause is not normally independent of the presence of other causes.
We can consider this very difficult to accept most times, where the probability of a particular feature is strictly correlated with another feature.
