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.
Bayes_rule
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.
–EOF (The Ultimate Computing & Technology Blog) —
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