Naive Bayes
A simple yet highly effective probabilistic classifier based on applying Bayes' theorem with strict independence assumptions between all input features.
Think of It Like This
Like a detective assuming that finding a suspect's fingerprint and shoe print are completely unrelated coincidences.
Despite the 'naive' assumption that features are conditionally independent given the class, this algorithm performs surprisingly well in practice. It is particularly famous for its historical success in spam filtering and text categorization. It requires very little training data and is extremely fast to train and evaluate.