Naive Bayes is a supervised classification method that relies on a simplifying assumption: explanatory variables are assumed to be independent from each other, even though in reality they are often correlated. It is this independence assumption that justifies the adjective “naive.”

When a new record needs to be classified, the model does not analyze all variables as a single block. Instead, it separately calculates the probability that each feature corresponds to a given class. These individual probabilities are then multiplied together and weighted by the overall probability of the class in the dataset. Finally, the category with the highest score is selected as the result.

Imagine the case of a red, sporty car with a powerful engine. A human being might quickly conclude that it is probably a Ferrari by combining these clues. Naive Bayes, however, works differently: it first calculates the probability that a red car is a Ferrari, then the probability that a sporty car is a Ferrari, and finally the probability that a large engine capacity corresponds to a Ferrari. These probabilities are multiplied together and weighted by the general proportion of Ferraris in the reference dataset. The model then concludes with the final category ; even if, in reality, these variables are not independent.

How the classification works ?

The functioning of Naive Bayes can be summarized in three steps. First, it estimates for each observed variable the probability of belonging to each possible class. Next, it combines these probabilities by multiplying them together, then multiplies the result by the overall probability of each class. Finally, it compares the scores obtained across classes and selects the one with the highest probability.

Let’s take the example of a spam filter. An email contains the words “money,” “free,” and “promotion.” The algorithm does not draw a direct conclusion from this combination. It separately calculates the probability that an email containing “money” is spam, then the probability for “free,” and then for “promotion.” These individual probabilities are multiplied together and weighted by the global proportion of spam in the dataset. If the final score exceeds that of the “normal email” class, the message is classified as spam.

This probabilistic logic can be applied across many domains. In text recognition, each word contributes individually to determining the topic of a document. In sentiment analysis, each positive or negative term separately influences the overall evaluation of a review. In finance, each indicator (price variation, trading volume, volatility) can be considered independently to estimate the probability that a transaction belongs to a risk class.

Naive Bayes offers several advantages in a professional context. Its simplicity of implementation and speed of calculation make it an efficient tool for handling large amounts of data, particularly text data. It requires relatively few examples to deliver useful results, which is valuable when available data is limited. Moreover, its interpretation remains accessible: each variable contributes distinctly to the calculation, making the model more transparent than other, more complex techniques.

However, this method relies on an independence assumption that is rarely true in practice. Explanatory variables are often correlated, which can bias the results. In addition, the model tends to perform less well when data is very heterogeneous or when certain classes are underrepresented. Finally, although the approach is robust for many applications, it is less suitable when the goal is to capture complex relationships between variables.