At the heart of the Naive Bayes algorithm lies the Bayes Theorem, which is a fundamental concept in probability theory. The theorem calculates the probability of an event occurring given prior knowledge of conditions related to the event.

It involves conditional probability and allows us to update our beliefs as we gather more evidence.

Applications of Naive Bayes Algorithm

1. Spam filtering: Naive Bayes has found great success in filtering out spam emails from the inbox. By analyzing the content and characteristics of an email, the algorithm can efficiently label it as spam or non-spam.
2. Text classification: Whether it’s sentiment analysis, topic categorization, or language identification, Naive Bayes has become a go-to choice for text classification tasks. Its effectiveness in handling large volumes of textual data and its speed make it a popular choice.
3. Recommendation systems: Naive Bayes also shines in recommendation systems, where it can predict user preferences based on their previous interactions. Whether it’s suggesting movies, music, or products, Naive Bayes can provide accurate recommendations.

How the Naive Bayes Algorithm Works