Naive Bayes | Supervised Learning | Day (7/45) | A2Z ML | Mohd Saqib

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Suppose, wer’e dealing with high-dimensional datasets, such as text classification. In text classification, there may be thousands or even millions of unique words, and each document can be represented as a high-dimensional vector of word counts or binary word indicators. A decision tree would have to split on each dimension (word) independently, leading to a very large and complex tree. In contrast, naive Bayes is well-suited for high-dimensional datasets because it makes the strong independence assumption between the features (words). This allows the algorithm to work with a much smaller and simpler model. Naive Bayes algorithms can also handle both continuous and discrete data

Naive Bayes is a family of simple probabilistic classifiers based on applying Bayes’ theorem with strong (naive) independence assumptions between the features. They are particularly suited for high-dimensional datasets, such as text classification and spam filtering, where the number of features (e.g. words) is much larger than the number of observations. This is because the independence assumption allows the algorithm to work with a much simpler model, making it efficient and fast.

Example: