What is Regression in Machine Learning with example?

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Introduction

Machine learning (ML) has a wide range of industrial applications that are likely to expand in the coming fields. The global machine-learning market is expected to reach USD 117 billion by 2027 at an impressive Compound Annual Growth Rate (CAGR) of 39%. Beginners and tech enthusiasts should know about machine learning concepts to improve skills and build a successful career in the ML industry. Regression algorithms in machine learning are an essential concept with many use cases.

Future values are predicted using regression algorithms in machine learning. Input/historical data is used to predict a wide range of future values using regression. A label in ML is defined as a target variable (to be predicted), and regression helps define the relationship between the label and the data points.

What is Machine Learning Regression?

Regression is a method for understanding the connection between independent variables or traits and a dependent variable or outcome. Outcomes can be predicted after estimating the relationship between the independent and dependent variables. Regression is a branch of statistics that forms a key part of predictive models in Machine Learning. It is used as an approach to predicting continuous results in predictive modeling, so it is useful in forecasting and predicting results from data. Machine learning regression typically involves plotting a line that best fits the data points. The distance between each point and the line is minimized to achieve a line of best fit.

Next to classification, regression is one of the main applications of the supervised type of machine learning.

Classification is the categorization of objects based on learned properties, while regression is the prediction of intermediate results. Both are predictive modeling problems. Supervised machine learning is an integral part of the approach in both cases, as the classification and regression models rely on labeled input and output training data. The features and training data output must be labeled so that the model understands the relationship.

What are Regression Models Used For?

Predictive analytics uses machine learning regression models primarily to forecast trends and outcomes. To comprehend the connection between the many independent variables and the result, regression models will undergo training. So the model can understand many factors that can lead to the desired result. The resulting models can be used in many ways and in different settings. Results can be predicted from new and unseen data, market fluctuations can be predicted and accounted for, and campaigns can be tested by tuning various independent variables.

Regression is used to identify patterns and relationships within a data set, which can then be applied to new and unseen data. This makes regression an essential element of machine learning in finance and is often used to predict portfolio performance or stock prices and trends. Models can be trained to understand the relationship between several features and the desired outcome. In most cases, machine learning regression provides organizations insight into specific results. But since this approach can influence an organization's decision-making process, the explainability of machine learning is an important consideration.

Common uses of machine learning regression models include:-

• Predicting ongoing outcomes such as house prices, stock prices, or sales.
• Forecasting the success of future retail sales or marketing campaigns to ensure efficient use of resources.
• Predicting customer or user trends, such as streaming services or e-commerce websites.
• Analysis of data sets to determine relationships between variables and output.
• Predicting interest rates or stock prices from various factors.
• Creating time series visualizations.

Our Learners Also Read: An Introduction to the Types Of Machine Learning

What Are The Types of Regression?

There are several different approaches used in machine learning to perform regression. Various popular algorithms are used to achieve machine learning regression. Other techniques may involve different numbers of independent variables or handle different types of data. Different machine learning regression models may also assume a different relationship between the independent and dependent variables. For example, linear regression techniques believe that the relationship is linear, so they would not be practical for data sets with non-linear relationships.

The following categories of regression analysis can be used to classify some of the most popular regression approaches in machine learning:

• Simple Linear Regression
• Multiple Linear Regression
• Logistic Regression
• Support Vector Regression 
• Decision Tree
• Random Forest

Simple Linear Regression

Simple linear regression is a technique that plots a straight line through the data points to minimize the error between the line and the data points. It is one of the simplest and most basic types of machine learning regression. In this case, the relationship between the independent and dependent variables is considered linear. This approach is simple because it examines the relationship between a dependent variable and one independent variable. Outliers can be a common occurrence in simple linear regression due to the line of best fit.

Multiple Linear Regression

When there are multiple independent variables, multiple linear regression is utilized. One of many linear regression methods is polynomial regression. When there are several independent variables, this kind of multiple linear regression is performed. It achieves a better fit than simple linear regression when numerous independent variables are involved. When plotted in two dimensions, the result would be a curved line fitted to the data points.

Logistic Regression

When the dependent variable may only take one of two possible values, such as true or false or success or failure, logistic regression is utilized. One can use logistic regression models to forecast the likelihood that a dependent variable will occur. In general, output values must be binary. A sigmoid curve can be used to chart the relationship between the dependent variable and the independent variables.

Support Vector Regression

A supervised learning approach called support vector machines can be applied to both classification and regression issues. As a result, it is known as support vector regression when applied to regression issues.

An effective regression method for continuous data is support vector regression. Some terms that are frequently used in support vector regression are listed below:-

Kernel: It is a function that maps lower dimensional data to higher dimensional data.

Hyperplane: Generally, in SVM, it is the dividing line between two classes, but in SVR, it is a line that helps to predict continuous variables and covers most of the data points.

Boundary Line: The two additional lines that the hyperplane draws around the data points to form a border are known as boundary lines.

The data points that are closest to the hyperplane and the opposing class are known as support vectors.

In SVR, we constantly seek the hyperplane with the largest margin, one that can accommodate the greatest amount of data points. The basic goal of SVR is to take into account as many data points as possible within the boundary lines, and the hyperplane (best-fit line) must contain as many data points as possible.

Decision Tree Regression

An approach for supervised learning called a decision tree can be used to address classification and regression issues.

It can resolve issues with both numerical and categorical data.

When using decision tree regression, a tree structure is produced, with each internal node standing in for an attribute that is being "tested," each branch for the test's conclusion, and each leaf node for the final choice or result.

Starting with the root node/parent node (the data set), a decision tree is created, which divides into left and right child nodes (subsets of the data set). These child nodes become the parent node of those nodes when they are further subdivided into their child nodes.

Random Forest Regression

A random forest is an ensemble strategy in which we take into account the forecasts of various decision regression trees.

K random points are chosen.

Choose n, where n is the total number of decision tree regressors that will be constructed. To generate numerous regression trees, repeat steps 1 and 2.

The diameter of each branch is assigned to a leaf node in each decision tree.

To predict the output for a variable, the average of all predictions of all decision trees is taken into account.

Random Forest avoids pruning (standard in decision trees) by creating random subsets of elements and building smaller trees using those subsets.

Conclusion

A Supervised Machine Learning method called regression is used to forecast continuous values. The ultimate goal of a regression algorithm is to plot the best-fit straight line or curve between the data. And in this blog, we have discussed some of the best algorithms used for regression.

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