Type of Regression in ML

There are various types of regressions which are used in data science and machine learning. Each type has its own importance on different scenarios, but at the core, all the regression methods analyze the effect of the independent variable on dependent variables. Here we are discussing some important types of regression which are given below:

• Linear Regression
• Logistic Regression
• Polynomial Regression
• Support Vector Regression
• Decision Tree Regression
• Random Forest Regression
• Ridge Regression
• Lasso Regression

#1. Linear Regression

• Linear regression is a statistical regression method which is used for predictive analysis.
• It is one of the very simple and easy algorithms which works on regression and shows the relationship between the continuous variables.
• It is used for solving the regression problem in machine learning.
• Linear regression shows the linear relationship between the independent variable (X-axis) and the dependent variable (Y-axis), hence called linear regression.
• If there is only one input variable (x), then such linear regression is called simple linear regression. And if there is more than one input variable, then such linear regression is called multiple linear regression.

#2. Logistic Regression

• Logistic regression is another supervised learning algorithm which is used to solve the classification problems. In classification problems, we have dependent variables in a binary or discrete format such as 0 or 1.
• Logistic regression algorithm works with the categorical variable such as 0 or 1, Yes or No, True or False, Spam or not spam, etc.
• It is a predictive analysis algorithm which works on the concept of probability.
• Logistic regression is a type of regression, but it is different from the linear regression algorithm in the term how they are used.

#3. Polynomial Regression

• Polynomial Regression is a type of regression which models the non-linear dataset using a linear model.
• It is similar to multiple linear regression, but it fits a non-linear curve between the value of x and corresponding conditional values of y.
• Suppose there is a dataset which consists of datapoints which are present in a non-linear fashion, so for such case, linear regression will not best fit to those datapoints. To cover such datapoints, we need Polynomial regression.

#4. Support Vector Regression

Support Vector Machine is a supervised learning algorithm which can be used for regression as well as classification problems. So if we use it for regression problems, then it is termed as Support Vector Regression.

Support Vector Regression is a regression algorithm which works for continuous variables. Below are some keywords which are used in Support Vector Regression:

• Kernel: It is a function used to map a lower-dimensional data into higher dimensional data.
• Hyperplane: In general SVM, it is a separation line between two classes, but in SVR, it is a line which helps to predict the continuous variables and cover most of the datapoints.
• Boundary line: Boundary lines are the two lines apart from hyperplane, which creates a margin for datapoints.
• Support vectors: Support vectors are the datapoints which are nearest to the hyperplane and opposite class.

In SVR, we always try to determine a hyperplane with a maximum margin, so that maximum number of
datapoints are covered in that margin. The main goal of SVR is to consider the maximum datapoints within the boundary lines and the hyperplane (best-fit line) must contain a maximum number of datapoints.

#1 Linear Regression

Linear regression is one of the easiest and most popular Machine Learning algorithms. It is a statistical method that is used for predictive analysis. Linear regression makes predictions for continuous/real or numeric variables such as sales, salary, age, product price, etc