How does Naïve Bayes handle high-dimensional sparse data in text classification systems?

Naïve Bayes performs well on high-dimensional sparse data because it estimates probabilities independently per feature, avoiding combinatorial complexity.

In text classification, each document can have tens of thousands of features (words), most of which are zero. Naïve Bayes exploits sparsity by modeling each feature independently given the class, avoiding the need for full covariance estimation (as in Gaussian models). This makes training and inference O(n_features) instead of exponential. Additionally, probability tables remain sparse and efficient to store, and smoothing ensures robustness against unseen words.