Naive Bayes
At first glance, Naive Bayes looks almost too simple to be useful. It applies Bayes’ theorem to classification problems, under the strong assumption that all features are independent of one another. This is rarely true in practice — yet the algorithm remains a workhorse in machine learning.
Why? Because it works. In email filtering, Naive Bayes classifiers have been the backbone of spam detection systems since the early 2000s. In text mining, they handle sentiment analysis and topic classification with surprising accuracy. The “naïve” assumption, while unrealistic, drastically simplifies computations, making the algorithm extremely fast, scalable, and easy to implement.
Its weaknesses show up in domains where dependencies between features are critical — for example, in image recognition, where pixels are highly correlated. Still, for high-dimensional and sparse data, Naive Bayes is hard to beat as a baseline model: quick to train, interpretable, and often good enough for production pipelines.
Naive Bayes belongs to the family of probabilistic classifiers and is often described as a “baseline that is hard to beat.” Despite its simplicity, it excels in domains where the independence assumption is not critically violated, especially in text classification, where words can be treated as approximately independent given a class.
There are several variants:
- Multinomial Naive Bayes, often used for word counts in text.
- Bernoulli Naive Bayes, suited for binary features like word presence or absence.
- Gaussian Naive Bayes, applied when features are continuous and assumed to follow a normal distribution.
One of its greatest strengths lies in its interpretability and efficiency: probabilities can be directly inspected, and training scales linearly with the number of samples and features. However, Naive Bayes can struggle when strong correlations exist or when probability estimates need calibration. Still, it remains a trusted benchmark in machine learning experiments and a workhorse in resource-constrained settings.
🔗 References:
- Zhang, The Optimality of Naive Bayes (AAAI, 2004)
