Contents
What are the assumptions for Naive Bayes classifier?
What is Naive Bayes algorithm? It is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.
Does naive Bayes use IID assumption?
Naive Bayes Classification is a supervised machine learning technique. It is simple but one of the most effective techniques of classification. Assumptions made in Naive Bayes: All the samples are i.i.d, i.e., all random variables are independent of each other and are drawn from the similar distribution.
Does naive Bayes have an objective function?
The objective function in the naive Bayes probability is to maximize the posterior probability given the training data in order to formulate the decision rule.
What is difference between classification and regression?
Fundamentally, classification is about predicting a label and regression is about predicting a quantity. That classification is the problem of predicting a discrete class label output for an example. That regression is the problem of predicting a continuous quantity output for an example.
What are the advantage of Naive Bayes?
Naive Bayes is suitable for solving multi-class prediction problems. If its assumption of the independence of features holds true, it can perform better than other models and requires much less training data. Naive Bayes is better suited for categorical input variables than numerical variables.
How does the naive Bayes classifier handle missing data in training?
When classifying instances, the attribute with the missing value is simply not included in the probability calculation ( reference) In training, the instance [with the missing data] is not included in frequency count for attribute value-class combination. ( reference)
Can a naive Bayes be extended to a Gaussian distribution?
Naive Bayes can be extended to real-valued attributes, most commonly by assuming a Gaussian distribution. This extension of naive Bayes is called Gaussian Naive Bayes.
How are probabilities stored in a naive Bayes model?
A list of probabilities are stored to file for a learned naive Bayes model. This includes: Class Probabilities: The probabilities of each class in the training dataset. Conditional Probabilities: The conditional probabilities of each input value given each class value. Learning a naive Bayes model from your training data is fast.
Is the naive Bayes assumption true for email?
The Naive Bayes assumption implies that the words in an email are conditionally independent, given that you know that an email is spam or not. Clearly this is not true. Neither the words of spam or not-spam emails are drawn independently at random. However, the resulting classifiers can work well in practice even if this assumption is violated.