Contents
How Naive Bayes deal with missing values?
Naive Bayes can handle missing data. Attributes are handled separately by the algorithm at both model construction time and prediction time. As such, if a data instance has a missing value for an attribute, it can be ignored while preparing the model, and ignored when a probability is calculated for a class value.
Does Naive Bayes have a loss function?
The loss function of naive Bayes is always the negative joint log-likelihood, -log p(X, Y). This choice of loss function, under the naive Bayes assumption of feature independence, makes naive Bayes fast: maximum-likelihood training can be done by performing one matrix multiplication and a few sums.
What makes naive Bayes classification so naive?
Naive Bayes is so ‘naive’ because it makes assumptions that are virtually impossible to see in real-life data and assumes that all the features are independent. Let’s take an example and implement the Naive Bayes Classifier, here we have a dataset that has been given to us and we’ve got a scatterplot which represents it.
What is the naive Bayes algorithm used for?
Naive Bayes is a probabilistic machine learning algorithm designed to accomplish classification tasks. It is currently being used in varieties of tasks such as sentiment prediction analysis, spam filtering and classification of documents etc.
When to use naive Bayes classifier?
Naive Bayes classifier is successfully used in various applications such as spam filtering, text classification, sentiment analysis, and recommender systems. It uses Bayes theorem of probability for prediction of unknown class.
What is “naive” in a naive Bayes classifier?
The first assumption of a Naive Bayes classifier is that the value of a particular feature is independent of the value of any other feature. Which means that the interdependencies within data are comfortably neglected. Hence the name ‘naive.’