Naive Bayes Classifier-explanation of Naive Bayes Classifier

Naive Bayes Classifier-

In machine learning the naive bayes classifier is most useful for finding outcomes with probability technique with use of bayes theorem. The new bayes algorithm are also updated for advanced technique in naive bayes classifier. The new buys algorithm is established in 1950 with text frequencies. The naive bayes classifier algorithm is the advanced method for calculating future outcomes in Real world. The naive bayes classifier are highly scalable, accurate for predict a value for future. In naive bayes classifier algorithm gives condition dependencies of that item for find particular predict values. That model is easy to build and particularly useful for very large data set along with simplicity and accuracy, It is highly sophisticated for classification method data mining.

The fallowing Equation is used for finding a naive bayes classifier-

P(C/X)=P(X/C).P(C)/P(X)

In that equation mainly-

P(C/X) is the posterior probability of class. P(C) is the prior probability of class.

P(X/C) is the likely-hood probability.

P(X) is the prior probability of predication.

The Naive Bayes Classifier gives more accuracy as compare to bayes classifier and bayes network.

Explanation :

The Naive Bayes Classifier is one of the most widely used algorithms in Data Warehousing and Data Mining (DWDM) for tasks related to classification and prediction. It is a probabilistic classifier based on Bayes’ Theorem, which provides a mathematical way to calculate the probability of a class or category based on given data attributes. The word “naive” indicates the simplifying assumption that all input features are independent of each other given the class label, even though in reality this may not always be true.

The algorithm works by using the training dataset to estimate the prior probabilities of different classes and the conditional probabilities of each attribute value given a class. When a new instance is encountered, the classifier applies Bayes’ Theorem to compute the posterior probability for each class and assigns the class with the highest probability to that instance. The formula for Bayes’ Theorem is:

[

P(C|X) = \frac{P(X|C) \times P(C)}{P(X)}

]

where:

• ( P(C|X) ) is the posterior probability of class ( C ) given the data ( X ),

• ( P(X|C) ) is the likelihood,

• ( P(C) ) is the prior probability of class ( C ), and

• ( P(X) ) is the prior probability of the data.

In classification, Naive Bayes is used to categorize data into predefined classes. For example, it can classify emails as spam or non-spam, or medical data as healthy or diseased. In prediction, it estimates the likelihood of an outcome based on historical data, such as predicting customer behavior or risk levels.

The Naive Bayes Classifier is highly efficient, easy to implement, and performs well with large datasets and high-dimensional data. Despite its naive independence assumption, it delivers accurate results in many real-world scenarios. Its simplicity, speed, and robustness make it a valuable tool in data mining, knowledge discovery, and decision support systems in data warehousing environments.

Read More-

1. What Is Data Warehouse
2. Applications of Data Warehouse, Types Of Data Warehouse
3. Architecture of Data Warehousing
4. Difference Between OLTP And OLAP
5. Python Notes