Types of Regression Analysis in Machine Learning
There are many types of regression analysis techniques, and the use of each method depends upon the number of factors. These factors include the type of target variable, shape of the regression line, and the number of independent variables.
 Linear RegressionThe linear regression model consists of a predictor variable and a dependent variable related linearly to each other.
The belowgiven equation is used to denote the linear regression model:
y=mx+c+e

Logistic Regression Logistic regression is one of the types of regression analysis technique, which gets used when the dependent variable is discrete. Example: 0 or 1, true or false, etc.
Below is the equation that denotes the logistic regression.logit(p) = ln(p/(1p)) = b0+b1X1+b2X2+b3X3….+bkXk

Ridge Regression Ridge Regression is used when there is a high correlation between the independent variables.
Below is the equation used to denote the Ridge Regressionβ = (X^{T}X + λ*I)^{1}X^{T}y
 Lasso Regression Lasso Regression performs regularization along with feature selection. It prohibits the absolute size of the regression coefficient.
Below is the equation that represents the Lasso Regression method:
N^{1}Σ^{N}_{i=1}f(x_{i}, y_{I}, α, β)
 Polynomial Regression In a polynomial regression, the power of the independent variable is more than 1.In this regression technique, the best fit line is not a straight line. It is rather a curve that fits into the data points.
Below equation represents the Polynomial Regression:
l = β0+ β0x1+ε
 Bayesian Linear Regression Bayesian Regression is used to find out the value of regression coefficients. In Bayesian linear regression, the posterior distribution of the features is determined instead of finding the leastsquares
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