# Naive Bayesalgorithm for predictive modeling.

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nagaswathi's
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2017-10-14 15:17

## Section

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.

Jason.

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