What is Naive Bayes?
Naive Bayes is a simple probabilistic classifier based on Bayes' theorem. It predicts the class of an item by calculating probabilities while assuming all features are independent of each other.
The algorithm starts from Bayes' theorem, which describes the probability of an event based on prior knowledge of conditions related to the event. It computes the posterior probability for each class given the input features.
The 'naive' part comes from the strong assumption that every feature is conditionally independent of every other feature. This simplifies calculations dramatically, allowing the model to multiply individual feature probabilities instead of modeling complex dependencies.
Different variants exist for different data types, such as Gaussian Naive Bayes for continuous features and Multinomial Naive Bayes for discrete counts like word frequencies in text.
Example
To classify an email as spam or not, Naive Bayes counts how often words like 'free' or 'winner' appear in spam versus normal emails, then multiplies those probabilities under the independence assumption to decide the most likely category.
Why it matters
Naive Bayes remains widely used today because it is extremely fast to train, works well with limited data, and serves as a strong baseline for text classification tasks such as spam filtering and sentiment analysis.
Frequently asked questions
It is called naive because it assumes all features are completely independent, which is rarely true in real data but greatly simplifies math.
Related terms
Supervised learning is a machine learning method where a model is trained on data that already has correct answers attached, so it can learn to predict those answers for new data.
Logistic Regression is a supervised machine learning algorithm used for binary classification that estimates the probability an input belongs to a particular class.
Active learning is a machine learning technique where the model itself selects the most informative unlabeled data points to be labeled by a human, rather than labeling data randomly or all at once.
Adam (Adaptive Moment Estimation) is a popular optimization algorithm used to train machine learning models by iteratively updating parameters based on gradients.
Anomaly detection is a machine learning technique that identifies rare or unusual data points that differ significantly from the majority of the data, often called outliers.
The bias-variance tradeoff describes the balance between two sources of error in a machine learning model: bias (error from overly simple assumptions) and variance (error from sensitivity to small fluctuations in the training data).