Naive Bayes is based on Bayes' Theorem, which provides a framework for calculating conditional probabilities and making predictions.

Naive Bayes assumes that the features are conditionally independent given the class label, simplifying the computation of posterior probabilities.

Naive Bayes employs Bayes' Theorem to calculate the posterior probability of a class given the observed features.

Naive Bayes is computationally efficient, making it suitable for large datasets and real-time applications.

Naive Bayes may struggle to capture complex relationships between features due to its assumption of conditional independence.

Naive Bayes performs well with sparse and high-dimensional data, where other methods may struggle due to the curse of dimensionality.

Combining feature selection, smoothing techniques, and incorporating prior knowledge can enhance the performance of Naive Bayes.

Naive Bayes is widely used for email spam filtering due to its efficiency and effectiveness in classifying emails as spam or legitimate.

The formula for calculating the posterior probability of a class given a set of features in Naive Bayes is $P(C | X) = \frac{P(X | C)P(C)}{P(X)}$.

Laplace Smoothing is a technique used to address the problem of zero probabilities in Naive Bayes by adding a small positive value to the count of each feature in each class.

Decision Tree Naive Bayes is not a variant of Naive Bayes. It is a hybrid algorithm that combines decision trees with Naive Bayes.

The computational complexity of training a Naive Bayes model is $O(n)$, where n is the number of training examples.

F1-score is not a measure of the performance of a Naive Bayes model. It is a measure of the performance of a binary classification model.

Maximum Likelihood Estimation is the algorithm used to learn the parameters of a Naive Bayes model.