Naive Bayesalgorithm for predictive modeling.

nagaswathi's version from 2017-10-14 15:17


Hi, Naive Bayes is a simple but surprisingly powerful algorithm for predictive modeling.


The model is comprised of two types of probabilities that can be calculated directly from your training data:


The probability of each class.
The conditional probability for each class given each x value.
Once calculated, the probability model can be used to make predictions for new data using Bayes Theorem.


When your data is real-valued it is common to assume a Gaussian distribution (bell curve) so that you can easily estimate these probabilities.


Naive Bayes is called naive because it assumes that each input variable is independent. This is a strong assumption and unrealistic for real data, nevertheless, the technique is very effective on a large range of complex problems.


In the next lesson, you will discover the K-Nearest Neighbors algorithm.