Gaussian Naive Bayes is a popular machine learning algorithm used for classification tasks. It is a variant of the Naive Bayes algorithm, which assumes that the features of a dataset are independent of each other. The term "Gaussian" refers to the assumption that the probability distribution of each feature is Gaussian or normal.

In Gaussian Naive Bayes, the algorithm calculates the probability of each feature for each class in the dataset. It then multiplies these probabilities to obtain the probability of a data point belonging to a particular class. The class with the highest probability is selected as the predicted class for the data point.

One of the advantages of Gaussian Naive Bayes is its simplicity and speed. It requires minimal training data and can handle both binary and multi-class classification problems. It also performs well in situations where there are many features and the data is highly skewed.

However, like any machine learning algorithm, Gaussian Naive Bayes has its limitations. It assumes that the features are independent, which may not be the case in some datasets. It also assumes that the variance of each feature is the same for all classes, which may not always hold true.

In summary, Gaussian Naive Bayes is a powerful machine learning algorithm that can be used for classification tasks. Its simplicity and speed make it a popular choice for many applications, but it is important to be aware of its limitations and use it appropriately.