What Are Regression Algorithms In Machine Learning?

February 27, 2023

Introduction

Regression algorithms are a type of machine learning algorithm used to predict a continuous numerical value. They are used in a variety of applications, such as predicting stock prices, forecasting sales, and predicting customer churn. In this blog post, we will explore what regression algorithms are and how they work.

What is Machine Learning?

Machine learning is a branch of artificial intelligence that uses algorithms to learn from data and make predictions. It is based on the idea that machines can learn from data without being explicitly programmed. Machine learning algorithms can be divided into two main categories: supervised learning and unsupervised learning. Supervised learning algorithms use labeled data to make predictions, while unsupervised learning algorithms use unlabeled data to discover patterns in the data.

What is Regression?

Regression is a type of supervised machine learning algorithm used to predict a continuous numerical value. It is based on the idea that given a set of input variables (x), we can use statistical methods to determine the relationship between those variables and an output variable (y). The goal of regression is to find the best fit line or curve that describes the relationship between x and y.

Types of Regression Algorithms

There are several types of regression algorithms used in machine learning, including linear regression, logistic regression, polynomial regression, decision tree regression, random forest regression, support vector machines (SVM) regression, and neural networks (NN) regression. Each type has its own strengths and weaknesses depending on the problem you’re trying to solve.

Linear Regression

Linear regression is one of the most commonly used types of regression algorithms. It models the relationship between two variables by fitting a linear equation to observed data points. The equation takes the form y = mx + b where m is the slope of the line and b is the intercept point where it crosses the y-axis. Linear regression can be used for both simple linear relationships as well as more complex non-linear relationships by using polynomial terms or other transformations on x or y values.

Logistic Regression

Logistic regression is another type of supervised machine learning algorithm used for classification problems where there are two possible outcomes (e.g., yes/no). It models the probability that an instance belongs to one class or another by fitting an S-shaped curve known as a logistic function or sigmoid function over observed data points. Logistic regression can also be extended to multi-class classification problems by using multiple logistic functions or softmax functions over observed data points.

Polynomial Regression

Polynomial regression is similar to linear regression but it uses polynomial terms instead of linear terms in its equation for fitting observed data points. This allows it to model more complex non-linear relationships between x and y values than linear regression can handle. Polynomial terms can be added up until they fit observed data points with sufficient accuracy for your application’s needs.

Decision Tree Regression

Decision tree regression uses decision trees as its underlying model for making predictions about continuous numerical values instead of discrete classes like logistic regression does for classification problems. Decision trees work by splitting up input variables into smaller subsets based on their values until they reach leaf nodes which contain predicted output values for each subset based on training data observations within them. Decision tree regressions can handle both simple linear relationships as well as more complex non-linear relationships depending on how deep their trees are built out before reaching leaf nodes with predicted output values in them for each subset created during splitting up input variables into smaller subsets based on their values during training time with training data observations within them at each level down from root node all way down until leaf nodes with predicted output values.

Random Forest Regression

Random forest regressions use multiple decision trees trained on different subsets of training data observations combined together into one model which makes predictions about continuous numerical outputs instead of discrete classes like logistic regressions do for classification problems like random forest classifications do . Random forests work by randomly selecting subsets of input variables when building out individual decision trees which helps reduce overfitting compared to single decision tree models since individual decision trees tend to overfit when trained only on single sets input variables . Random forests also help reduce variance compared single decision tree models since individual decision trees tend have high variance when trained only single sets input variables .

Support Vector Machines (SVM) Regression

Support vector machines (SVMs) are another type supervised machine learning algorithm used for both classification problems like SVMs classifications do as well as continuous numerical outputs like SVMs regressions do . SVMs work by mapping input variables onto higher dimensional feature spaces using kernels then finding optimal hyperplanes which best separate different classes or predict continuous numerical outputs depending whether you’re using SVMs classifications or SVMs regressions respectively . SVMs have been shown outperform other types supervised machine learning algorithms including neural networks some cases due their ability capture non-linear relationships between inputs outputs better than other types supervised machine learning algorithms .

Neural Networks (NN) Regression

Neural networks (NNs) are another type supervised machine learning algorithm used predict continuous numerical outputs instead discrete classes like NNs classifications do . NNs work by connecting neurons together into layers then passing inputs through layers neurons connected together form networks which then make predictions about continuous numerical outputs instead discrete classes like NNs classifications do . NNs have been shown outperform other types supervised machine learning algorithms including support vector machines some cases due their ability capture more complex non-linear relationships between inputs outputs better than other types supervised machine learning algorithms .

Conclusion

In conclusion, there are several types of regression algorithms used in machine learning such as linear, logistic, polynomial, decision tree, random forest, support vector machines (SVM), and neural networks (NN). Each type has its own strengths and weaknesses depending on what problem you’re trying to solve so it’s important understand how they work before deciding which one use your application’s needs best .