When applying Naive Bayes, what method is often used to handle zero probabilities?

In the context of Naive Bayes classification, the method commonly used to handle zero probabilities is known as smoothing. Naive Bayes relies on the assumption of independence among features and calculates probabilities based on the frequency of features in the training data. If a particular feature does not occur in the training set for a given class, the probability estimate for that feature given the class becomes zero. This poses a problem because multiplying probabilities together for predictions can lead to zero probability overall, eliminating any chance of that class being predicted.

Smoothing techniques, such as Laplace smoothing (also known as additive smoothing), are employed to address this issue. By adding a small constant (usually 1) to the counts of occurrences for each feature, we ensure that no probability becomes exactly zero, thereby allowing the model to make predictions even when unseen features are encountered in new data.

This approach is essential for improving the robustness of the Naive Bayes classifier, especially in situations where the dataset may be sparse, or when dealing with categorical variables that have many unique values. Other methods listed, such as scaling, normalization, and feature selection, do not specifically address the issue of zero probabilities in the context of Naive Bayes classifiers.