Contents
What kind of problem is naive Bayes used for?
Naive Bayes is a probabilistic algorithm that’s typically used for classification problems. Naive Bayes is simple, intuitive, and yet performs surprisingly well in many cases. For example, spam filters Email app uses are built on Naive Bayes.
What is the Laplace estimator for naive Bayes?
One simple way to fix this problem is called Laplace Estimator: add imaginary samples (usually one) to each category For continuous features, there are essentially two choices: discretization and continuous Naive Bayes. Discretization works by breaking the data into categorical values.
Can a spam filter be built on Naive Bayes?
Naive Bayes is simple, intuitive, and yet performs surprisingly well in many cases. For example, spam filters Email app uses are built on Naive Bayes. In this article, I’ll explain the rationales behind Naive Bayes and build a spam filter in Python. (For simplicity, I’ll focus on binary classification problems)
Why is the posterior probability of naive Bayes zero?
This means that Naive Bayes handles high-dimensional data well. For categorical features, the estimation of P (Xi|Y) is easy. However, one issue is that if some feature values never show (maybe lack of data), their likelihood will be zero, which makes the whole posterior probability zero.
What kind of algorithm is naive Bayes classifier?
Naive Bayes classifiers are a collection of classification algorithms based on Bayes’ Theorem. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other.
Which is the first probability in naive Bayes?
The first probability, P (IsCat), is the prior and the second probability is the scaler — and as we just learned, the product of prior and scaler is a joint probability: So we can rewrite our joint probability as: Almost there! This is where naive Bayes’ simplifying assumption comes to save the day.