Naive Bayes Classification Full Explanation with examples | Supervised Learning

Naive Bayes is a family of probabilistic algorithms based on Bayes' Theorem, particularly suited for classification tasks. The "naive" assumption in Naive Bayes is that all features are independent given the class label, which simplifies the computation but might not hold true in real-world data.
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Learn Naive Bayes Algorithm, one of the most powerful and simple probabilistic machine learning models! In this video, I’ll explain how Naive Bayes works, with a real-world example to make it easy to understand. Whether you're studying for exams or want to sharpen your data science skills, this tutorial will give you the knowledge to implement Naive Bayes in your own projects. Stay tuned till the end for bonus tips on improving its performance. Don't forget to subscribe for more machine learning tutorials!
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