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There are two types of statistical-based algorithms which are as follows −

**Regression**− Regression issues deal with the evaluation of an output value located on input values. When utilized for classification, the input values are values from the database and the output values define the classes. Regression can be used to clarify classification issues, but it is used for different applications including forecasting. The elementary form of regression is simple linear regression that includes only one predictor and a prediction.Regression can be used to implement classification using two various methods which are as follows −

**Division**− The data are divided into regions located on class.**Prediction**− Formulas are created to predict the output class’s value.

**Bayesian Classification**− Statistical classifiers are used for the classification. Bayesian classification is based on the Bayes theorem. Bayesian classifiers view high efficiency and speed when used to high databases.**Bayes Theorem**− Let X be a data tuple. In the Bayesian method, X is treated as “evidence.” Let H be some hypothesis, including that the data tuple X belongs to a particularized class C. The probability P (H|X) is decided to define the data. This probability P (H|X) is the probability that hypothesis H’s influence has given the “evidence” or noticed data tuple X.P (H|X) is the posterior probability of H conditioned on X. For instance, consider the nature of data tuples is limited to users defined by the attribute age and income, commonly, and that X is 30 years old users with Rs. 20,000 income. Assume that H is the hypothesis that the user will purchase a computer. Thus P (H|X) reverses the probability that user X will purchase a computer given that the user’s age and income are acknowledged.

P (H) is the prior probability of H. For instance, this is the probability that any given user will purchase a computer, regardless of age, income, or some other data. The posterior probability P (H|X) is located on more data than the prior probability P (H), which is free of X.

Likewise, P (X|H) is the posterior probability of X conditioned on H. It is the probability that a user X is 30 years old and gains Rs. 20,000.

P (H), P (X|H), and P (X) can be measured from the given information. Bayes theorem supports a method of computing the posterior probability P (H|X), from P (H), P (X|H), and P(X). It is given by

$$P(H|X)=\frac{P(X|H)P(H)}{P(X)}$$

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