Naive Bayes
Naive Bayes is an algorithm within Explainable AI, which focuses on simplicity and transparency in its decision-making process. It is based on applying Bayes' theorem with independence assumptions between predictors. This model can be applied to classification tasks where it calculates the probability of a given data point belonging to a certain class, using prior knowledge about the distribution of classes and features. The "naive" nature comes from assuming that each feature is conditionally independent given the class label, simplifying the computation without sacrificing much accuracy in many real-world scenarios. Due to its simplicity, Naive Bayes provides clear insights into the model's decision-making process, making it suitable for Artificial Intelligence Algorithms under Explainable AI within Technology and Artificial Intelligence domains.