Naïve Bayes

This chapter introduces the Naïve Bayes algorithm, a predictive model based on Bayesian analysis. The chapter starts with a thought problem involving a breathalyzer used by a police department. It demonstrates how Bayes’ Theorem can be used to estimate the probability that a driver is over the legal alcohol limit based on breathalyzer results. The concept of Naïve Bayes is illustrated with a toy data set of 15 observations, demonstrating how probabilities are calculated using counting. The assumption of conditional independence is explained, and the algorithm’s applicability to categorical and continuous predictors is discussed. The problem of zero probabilities and the need for Laplace smoothing to avoid issues with small sample sizes are explored. Naïve Bayes in KNIME is applied to a real-world example of predicting heart disease detection and identifying spam emails, achieving 85% and 99% accuracy, respectively, with test data. Despite its strong assumption of independence among predictors, Naïve Bayes proves to be a practical and efficient algorithm for classification tasks, especially when dealing with a large number of predictors. While its probability estimates may not always be precise, the classification results are often reliable.