What type of data preprocessing is necessary before applying Naive Bayes?

Before applying Naive Bayes, encoding categorical features is indeed a necessary step. Naive Bayes is a probabilistic classifier that works on the principle of applying Bayes' theorem, assuming independence among predictors. Most implementations of Naive Bayes require numerical input, and if your features include categorical data (like strings or labels), these need to be converted into a numerical format for the algorithm to process them effectively.

Encoding methods like one-hot encoding or label encoding render categorical variables into a format that can be understood and utilized by the algorithm, allowing it to compute probabilities accurately based on the input feature set. Without this step, the model would not be able to interpret the data correctly, ultimately leading to inaccurate predictions or errors during the model training phase.

While normalization of numerical features and feature scaling can be important in many machine learning algorithms—especially those that rely on distance measures—Naive Bayes does not require these preprocessing steps because it does not make assumptions about the distribution of features and works with raw frequency counts. Therefore, focusing on the necessity of encoding categorical features highlights the key aspect of preparing data specifically for the Naive Bayes classifier.