![]() ![]() So the hard part in all of this is drawing the “best” straight line through the original training dataset. Linear regression is about finding the “best fit” line The critical step though is drawing the “best” line through your training data. I’m simplifying a little, but that’s essentially it. Use the equation for the line as a “model” to make predictions.Draw the “best fit” line through the training data.This should give you a good conceptual foundation of how linear regression works. If you know the value, you can compute the predicted output value, by using the formula. To make a prediction with our simple linear regression model, we’re just need to use that datapoint as an input to our linear equation. This equation is effectively a model that we can use that linear model to make predictions.įor example, let’s say that after building the model (i.e., drawing a line through the training data), we have a new input value. Remember: a line that we draw through the data will have an equation associated with it. When we draw such a line through the training dataset, we’ll essentially have a little model of the form by using the formula. By the way, this input dataset is typically called a training dataset in machine learning and model building. To do this, we use an existing dataset as “training examples.” When we draw a line through those datapoints, we’re “training” a linear regression model. To clarify this a little more, let’s look at simple linear regression visually.Įssentially, when we use linear regression, we’re making predictions by drawing straight lines through a dataset. In linear regression, we’re making predictions by drawing straight lines The equation for linear regression is essentially the same, except the symbols are a little different:īasically, this is just the equation for a line. If you’ve taken highschool algebra, you probably remember the equation for a line,, where is the slope and is the intercept. If you haven’t seen this before, don’t let the symbols intimidate you. Specifically, we assume that there is a linear relationship between Y and X as follows: We make the assumption that we can predict Y by using X. We have two variables in a dataset, X and Y. Let’s start with the simplest case of simple linear regression. ![]() Linear regression is fairly straight forward. A quick review of linear regression concepts In this blog post, I’ll show you how to do linear regression in R.īefore I actually show you the nuts and bolts of linear regression in R though, let’s quickly review the basic concepts of linear regression. It’s a technique that almost every data scientist needs to know.Īlthough machine learning and artificial intelligence have developed much more sophisticated techniques, linear regression is still a tried-and-true staple of data science. ![]()
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