August 21, 2022
August 16, 2022

# Regression in Machine Learning INTRODUTION TO REGRESSION IN MACHINE LEARNING:

The following article provides an outline for Regression in Machine Learning. Regression means to predict the value using the input data. Regression models are used to predict a continuous value. It is mostly used to find the relationship between the variables and forecasting. Regression models differ based on the kind of relationship between dependent and independent variables.

Types of Regression in Machine Learning:

There are different types of regression:

1. Simple Linear Regression: Simple linear regression is a target variable based on the independent variables. Linear regression is a machine learning algorithm based on supervised learning which performs the regression task.
2. Polynomial Regression: Polynomial regression transforms the original features into polynomial features of a given degree or variable and then apply linear regression to it.
3. Support Vector Regression: Support vector regression identifies a hyperplane with the maximum margin such that the maximum number of data points is within the margin.
4. Decision Tree Regression: The decision tree is a tree that is built by partitioning the data into subsets containing instances with similar values. It can use for regression and classification also.
5. Random Forest Regression: Random forest is an ensemble approach where we take into account the predictions of several decision regression trees.

Implementation of Linear Regression in Machine Learning

Linear regression is employed in varied ways in which a number of them are listed as:

• Sales prognostication
• Risk analysis
• Housing applications
• Finance applications

The process used for implementing the statistical regression whereas exploitation it in many ways in which some are mentioned below:

• Exploring the data
• Slicing the data
• Train and split data
• Generate the model
• Evacuate the accuracy