Linear regression is the most common introduction to machine learning. The basic model fits a straight line. It assume a direct relationship. For example, you can have a list of heights and weight. If you assume the taller people are, the heavier they are, this would be a stright line/linear relationship. In this article, we will learn how to build a basic linear regression model with Sklearn.
The assumption is good, but doesn't capture everything. Modified linear regression models can help with these variations. We will look at these in the future.
To build (fit) a linear regression model we use the
LinearRegression class. We create an instance of the class, then we use the
fit method. We pass the features (x) and target (y) to this method. The
fit function then returns a model that is our linear regression model.
from sklearn.linear_model import LinearRegression from sklearn.datasets import load_boston boston = load_boston() features = boston.data target = boston.target regression = LinearRegression() model = regression.fit(features, target)