Feature Engineering and Selection for Regression Models with Python and Scikit-learn
Training a machine learning model is like baking a cake: the quality of the end result depends on the ingredients … Read more
Simple Regression
Here you’ll find all articles that use simple linear regression.
Simple linear regression is a statistical method for studying the relationship between two continuous variables. One of the variables is the dependent variable, and the other is the independent variable. The goal is to find the line of best fit that describes the relationship between the two variables. This is typically done using the least squares method. This method finds the line that minimizes the sum of the squares of the distances between the data points and the line. Once it has determined the line of best fit, it can use the line to make predictions about the value of the dependent variable based on the value of the independent variable. For example, if the dependent variable is the price of a house and the independent variable is the size of the house, the line of best fit could be used to predict the price of a house based on its size. An advantage of linear regression is that it is easy to use and interpret.