Can we use naive Bayes for continuous data?
There are two ways to estimate the class-conditional probabilities for continuous attributes in naive Bayes classifiers: We can discretize each continuous attribute and then replace the continuous attribute value with its corresponding discrete interval.
What is the zero frequency problem in naïve Bayes and how can it be dealt with?
The zero-frequency problem And this will get a zero when all the probabilities are multiplied. An approach to overcome this ‘zero-frequency problem’ in a Bayesian environment is to add one to the count for every attribute value-class combination when an attribute value doesn’t occur with every class value.
Why is naive Bayes Naive?
Naive Bayes is a simple and powerful algorithm for predictive modeling. Naive Bayes is called naive because it assumes that each input variable is independent. This is a strong assumption and unrealistic for real data; however, the technique is very effective on a large range of complex problems.
How to perform classification using naive Bayes classifier?
It performs well in case of categorical data as compared to numeric data. So, how do we perform classification using Naive Bayes when the data we have is continuous in nature.
How does naive Bayes work with continuous variables?
In no way must x be discrete. For example, Gaussian Naive Bayes assumes each category C has a different mean and variance: density p ( x ∣ C = i) = ϕ ( μ i, σ i 2). There are different ways to estimate the parameters, but typically one might:
What is the zero frequency problem in Bayes classifier?
If an instance in test data set has a category that was not present during training then it will assign it “Zero” probability and won’t be able to make prediction. This is known as Zero frequency problem. It skews the whole performance of the classification.
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.