Why is Naive Bayes often preferred for text classification?

Naive Bayes is often preferred for text classification primarily because it works well with high dimensional datasets. Text data, especially in natural language processing, usually consists of a vast number of features—each unique word in a document can be considered a feature. Naive Bayes efficiently handles this dimensionality by applying the assumption of conditional independence among the features, which simplifies calculations and allows the model to remain computationally efficient even as the number of features increases.

Furthermore, this algorithm is particularly effective for text classification tasks such as spam detection or sentiment analysis, where the presence of many words (features) is common and important for making predictions. Its ability to handle a large feature space while maintaining speed and efficiency makes it a strong choice in scenarios where traditional models might struggle with computational costs or overfitting due to high dimensionality.

While other characteristics, such as model interpretability and its requirement for minimal tuning, can also be advantageous, the core strength of Naive Bayes in the context of high-dimensional data is critical for its popular usage in text classification tasks.