Naive Bayes Algorithm with Python Concepts of Naive Bayes Naive Bayes is a classification algorithm based on Bayes' theorem, which states that the probability of a hypothesis is updated by considering new evidence. Since it presumes that all features are independent of one another, which may not always be the case in real-world datasets, it is known as a "naive". Despite this limitation, Naive Bayes is widely used in text classification, spam filtering, and sentiment analysis. Naive Bayes Algorithm Define the problem and collect data. Choose a hypothesis class (e.g., Naive Bayes). Compute the prior probability and likelihood of each class based on the training data. Use Bayes' theorem to compute the posterior probability of each class given the input features. Classify the input by choosing the class with the highest posterior probability. Evaluate the model on a test dataset to estimate its performance. Here's an example code in Python for Naive Bayes: Python cod