Assumption of Naive Bayes
The fundamental Naive Bayes assumption is that each feature makes an:
- Feature independence: The features of the data are conditionally independent of each other, given the class label.
- Continuous features are normally distributed: If a feature is continuous, then it is assumed to be normally distributed within each class.
- Discrete features have multinomial distributions: If a feature is discrete, then it is assumed to have a multinomial distribution within each class.
- Features are equally important: All features are assumed to contribute equally to the prediction of the class label.
- No missing data: The data should not contain any missing values.
With relation to our dataset, this concept can be understood as:
- We assume that no pair of features are dependent. For example, the temperature being ‘Hot’ has nothing to do with the humidity or the outlook being ‘Rainy’ has no effect on the winds. Hence, the features are assumed to be independent.
- Secondly, each feature is given the same weight(or importance). For example, knowing only temperature and humidity alone can’t predict the outcome accurately. None of the attributes is irrelevant and assumed to be contributing equally to the outcome.
The assumptions made by Naive Bayes are not generally correct in real-world situations. In-fact, the independence assumption is never correct but often works well in practice.Now, before moving to the formula for Naive Bayes, it is important to know about Bayes’ theorem.
Naive Bayes Classifiers
A Naive Bayes classifiers, a family of algorithms based on Bayes’ Theorem. Despite the “naive” assumption of feature independence, these classifiers are widely utilized for their simplicity and efficiency in machine learning. The article delves into theory, implementation, and applications, shedding light on their practical utility despite oversimplified assumptions.