What is prior probability in naive Bayes?

What is prior probability in naive Bayes?

Prior probability, in Bayesian statistical inference, is the probability of an event before new data is collected. This is the best rational assessment of the probability of an outcome based on the current knowledge before an experiment is performed.

How is naive Bayes algorithm implemented?

Naive Bayes Tutorial (in 5 easy steps)

1. Step 1: Separate By Class.
2. Step 2: Summarize Dataset.
3. Step 3: Summarize Data By Class.
4. Step 4: Gaussian Probability Density Function.
5. Step 5: Class Probabilities.

Why do we use naive Bayes algorithm?

Pros: It is easy and fast to predict class of test data set. It also perform well in multi class prediction. When assumption of independence holds, a Naive Bayes classifier performs better compare to other models like logistic regression and you need less training data.

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.

Are there any real world uses for naive Bayes?

In spite of their apparently over-simplified assumptions, naive Bayes classifiers have worked quite well in many real-world situations, famously document classification and spam filtering. They require a small amount of training data to estimate the necessary parameters.

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.

What kind of distribution does a Gaussian naive Baye have?

In Gaussian Naive Bayes, continuous values associated with each feature are assumed to be distributed according to a Gaussian distribution. A Gaussian distribution is also called Normal distribution. When plotted, it gives a bell shaped curve which is symmetric about the mean of the feature values as shown below: