A supervised machine learning algorithm based on possibilities.
Naive Bayes is called “Naive” because it knows the frequency of usage of the words, not word order.
One particular feature of Naive Bayes is that it’s a good algorithm for working with text classification. When dealing with text, it’s very common to treat each unique word as a feature, and since the typical person’s vocabulary is many thousands of words, this makes for a large number of features. The relative simplicity of the algorithm and the independent features assumption of Naive Bayes make it a strong performer for classifying texts.
- Easy to implement.
- Simple to run, it’s efficient.
- Phrases in multiple words like “Chicago Bulls” doesn’t work,