Algorithms used in the process of machine learning have a number of different functions. They are often used to map data, make sense of large quantities of data, and predict developments over time. There are few algorithms which can conceivably perform all three tasks. One of these is linear regression. This algorithm was originally developed for statistical processing the 19th century. In recent years, it has become a key part of data mining. A linear regression algorithm’s primary function is to draw a line. However, that line has such a variety of uses that it can be considered part of one of the most consequential machine learning algorithms in use today.

## Linear Regression Defined

The linear regression algorithm is an algorithm that helps point out patterns in the development of a set of data. This process works by discovering the mean out of a considerable data set. It uses the least squares function to learn the mean and then map it out onto the rest of the data points. Other functions can also be used to connect data points and to smooth out the differences between those points.

The principle at work is regression to the mean. Regression to the mean refers to the tendency of data sets to eventually revert back to a general mean even if there are extreme outliers. Those outliers can then be visualized and identified. The linear regression algorithm figures out means between the outliers and then creates a basic function that draws a line segment through the data points. As a linear function, it has a continuous slope and y-intercept that continue indefinitely in both directions. The product of linear regression is a simple line that shows a tendency for what might be an otherwise complex and disparate amount of data.

This algorithm must be attached to a series of computers or computer functions in order to work properly and handle large amounts of complex data. A common artificial learning architecture that implements linear regression is the artificial neural network. Artificial neural networks attempt to mimic the workings of the human brain using a series of nodes. These nodes perform different functions and are weighted differently.

For linear regression, each node may process part of the function or contribute to developing the proper function. The artificial intelligence program would then change the weights of the nodes to change the function and the slope of the line that was created. As a result, the linear regression line could shift depending on the intake of new information.

## Linear Regression Types

There are many different ways to use a machine learning algorithm to process linear regression. One of these ways is through supervised learning. Supervised learning involves some sort of artificial intelligence architecture to process an algorithm based on an example set. In the case of a linear regression algorithm, the example set would be an initially established regression line. The algorithm would attempt to redraw that exact line within a small margin of error. If the artificial intelligence system fails at first, it can make thousands of attempts and slowly change weights or its formula in order to redraw the initial line. Then, the line can be updated by the operator as time goes on and the system processes more data.

Unsupervised learning is another approach that can be used with a linear regression algorithm. In unsupervised learning, there is no example set for the artificial intelligence framework to follow. Instead, there is a simple set of guidelines and the assigned task of processing a linear regression algorithm and drawing a regression line. The machine learning software develops and advances as it processes the algorithm thousands of times. Eventually, the machine learning algorithm takes the linear regression model to places that the operator may not have been able to previously predict.

## Linear Regression Uses

Linear regression has a wide array of uses in the field of data mining and artificial intelligence. It is a basic tool that improves the understanding of large amounts of data. Thousands or millions of data points can be reduced to a simple line on a plot. Identifying outliers can be critical in sorting and understanding data.

The visualization of a regression line helps individuals see which outliers are applicable and which ones are not. Data can then more easily be sorted. A regression line can be used to set up later binary categories and further algorithms. Binary categories could be crafted by breaking up data into points that were above and below the regression line.

The line is also continuous and endless. This line is focused on means and not on specific locations of a data set. It points to a trend rather than a particular set of data. As a result, it can be an important predictive tool. The linear regression line can be used to analyze and predict each new data point that emerges in the system. It can then be changed and predict new data points as circumstances change over time.