# Machine Learning Regression Models

## What is Regression Techniques?

Regression algorithms are used to predict a continuous value. Predicting house prices given the features of the house as the number of rooms, number of beds, number of washrooms, size of house, price, etc. is one of the common examples of Regression. It is a supervised technique. A detailed explanation of different types of Machine Learning techniques is already explained by desireAI. In this article, we will learn about Machine Learning Regression Models.

### Aim of ‘Machine Learning Regression Models’ article is

1. What is Regression Techniques?
2. Real-Life Applications of Regression Algorithms
3. Types of Regression in Machine Learning
4. Key Terms to Know in Regression Model

## Applications of Regression Algorithms

Some of the key areas where Regression in Supervised Learning are being used:

• Financial portfolio prediction
• Salary forecasting
• Real estate predictions
• To perform stress test platforms to analyze multiple stress scenarios
• Customer lifetime value in the insurance sector and are leveraged to produce a continuous outcome

## Types of Regression in Machine Learning

List of most commonly used machine learning regression models are given below: –

• Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Decision Tree Regression
• Random Forest Regression

## Key Terms to Know in Regression Model

When trying to interpret the results of the regression model, you must understand the algorithms. As well we have to learn some of the most common terms used in regression analysis.

• Feature: A feature or input is an individual measurable property of a phenomenon being observed.
• Estimator: An equation or algorithm for generating estimates of parameters, given relevant information or data.
• Bias: An estimate is unbiased if its expectation equals the value of the parameter being estimated; otherwise it is biased.
• Efficiency: An estimator A is more efficient than an estimator B if A has a smaller sampling variance i.e. if the particular values generated by A are more tightly clustered around their expectation.
• F-test: A common process for jointly testing a set of linear restrictions on a regression algorithm.

## The conclusion to Machine Learning Regression Models

This completes the ‘Machine Learning Regression Models’ tutorial. We have learned about different Regression algorithms used in Machine Learning and application of Regression algorithms. In the upcoming articles, we will learn about different machine learning regression models in detail. Including their real-life use and how we implement those regression algorithms in the dataset. Check out our article on Introduction to Classification for getting the main difference between classification and regression.

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