What is a Machine Learning Linear regression?

 Linear regression is a way to find the relationship between two things. For example, you might want to know how the size of a toy car affects how fast it can go. You could measure the sizes and speeds of a bunch of toy cars, and then use linear regression to find a line that best fits the data.

Imagine you have a graph with two axes: the x-axis and the y-axis. The x-axis represents the size of the car, and the y-axis represents how fast it can go. You can plot the data from your toy cars on the graph. Each car will have a dot on the graph, and the dot will be placed at the point where the car’s size and speed intersect.

Linear regression is about finding a line that goes through as many of the dots as possible. This line is called the “line of best fit.” It’s not always possible to find a line that goes through every single dot, but linear regression helps us find the line that comes closest.

Once you have the line of best fit, you can use it to predict how fast a car will go based on its size. For example, if you have a toy car that’s 10 inches long, you can use the line of best fit to predict that it will go about 20 miles per hour.

Linear regression is a powerful tool that can be used to find relationships between all sorts of things. For example, it can be used to predict how well a student will do on a test based on their grades in class, or how many sales a company will make based on their advertising budget.

Here is a simple analogy that you can use to explain linear regression:

Imagine you have a bag of marbles. You want to know how far the marbles will roll if you drop them from different heights. You could drop a marble from a height of 1 foot and measure how far it rolls. Then you could drop a marble from a height of 2 feet and measure how far it rolls. And so on.

If you plot the data on a graph, you would see that the points form a line. This line is the line of best fit. It shows you how the distance that the marble rolls changes as the height from which it is dropped increases.

Source: Partner Website - QUE.com Artificial Intelligence.