How To Run Linear Regressions In Python Scikitlearn
Before we start: This Python tutorial is a part of our series of Python Package tutorials.
Scikitlearn is a Python package that simplifies the implementation of a wide range of Machine Learning (ML) methods for predictive data analysis, including linear regression.
Linear regression can be thought of as finding the straight line that best fits a set of scattered data points:
You can then project that line to predict new data points. Linear regression is a fundamental ML algorithm due to its comparatively simple and core properties.
Linear Regression Concepts
A basic understanding of statistical math is key to comprehending linear regression, as is a good grounding in ML concepts.
For more information on ML concepts and terminology, refer to: What is ScikitLearn In Python?
The following are some key concepts you will come across when you work with scikitlearn’s linear regression method:
 Best Fit – the straight line in a plot that minimizes the deviation between related scattered data points.
 Coefficient – also known as a parameter, is the factor a variable is multiplied by. In linear regression, a coefficient represents changes in a Response Variable (see below).
 Coefficient of Determination – the correlation coefficient denoted as 𝑅². Used to describe the precision or degree of fit in a regression.
 Correlation – the relationship between two variables in terms of quantifiable strength and degree, often referred to as the ‘degree of correlation’. Values range between 1.0 and 1.0.
 Dependent Feature – a variable denoted as y in the slope equation y=ax+b. Also known as an Output, or a Response.
 Estimated Regression Line – the straight line that best fits a set of scattered data points.
 Independent Feature – a variable denoted as x in the slope equation y=ax+b. Also known as an Input, or a predictor.
 Intercept – the location where the Slope intercepts the Yaxis denoted b in the slope equation y=ax+b.
 Least Squares – a method of estimating a Best Fit to data, by minimizing the sum of the squares of the differences between observed and estimated values.
 Mean – an average of a set of numbers, but in linear regression, Mean is modeled by a linear function.
 Ordinary Least Squares Regression (OLS) – more commonly known as Linear Regression.
 Residual – vertical distance between a data point and the line of regression (see Residual in Figure 1 below).
 Regression – estimate of predictive change in a variable in relation to changes in other variables (see Predicted Response in Figure 1 below).
 Regression Model – the ideal formula for approximating a regression.
 Response Variables – includes both the Predicted Response (the value predicted by the regression) and the Actual Response, which is the actual value of the data point (see Figure 1 below).
 Slope – the steepness of a line of regression. Slope and Intercept can be used to define the linear relationship between two variables: y=ax+b.
 Simple Linear Regression – a linear regression that has a single independent variable.
Figure 1. Illustration of some of the concepts and terminology defined in the above section, and used in linear regression:
Linear Regression Class Definition
A scikitlearn linear regression script begins by importing the LinearRegression class:
from sklearn.linear_model import LinearRegression sklearn.linear_model.LinearRegression()
Although the class is not visible in the script, it contains default parameters that do the heavy lifting for simple least squares linear regression:
sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True)
Parameters:

fit_interceptbool, default=True
Calculate the intercept for the model. If set to False, no intercept will be used in the calculation.

normalizebool, default=False
Converts an input value to a boolean.

copy_Xbool, default=True
Copies the X value. If True, X will be copied; else it may be overwritten.
How to Create a Linear Regression Model
In this example, a linear regression model is created based on data in a numpy array. The coefficients are formulated and then printed in the console:
# Import the packages and classes needed in this example:
import numpy as np
from sklearn.linear_model import LinearRegression
# Create a numpy array of data:
x = np.array([6, 16, 26, 36, 46, 56]).reshape((1, 1))
y = np.array([4, 23, 10, 12, 22, 35])
# Create an instance of a linear regression model and fit it to the data with the fit() function:
model = LinearRegression().fit(x, y)
# The following section will get results by interpreting the created instance:
# Obtain the coefficient of determination by calling the model with the score() function, then print the coefficient:
r_sq = model.score(x, y)
print('coefficient of determination:', r_sq)
# Print the Intercept:
print('intercept:', model.intercept_)
# Print the Slope:
print('slope:', model.coef_)
# Predict a Response and print it:
y_pred = model.predict(x)
print('Predicted response:', y_pred, sep='\n')
Watch how to create a Linear Regression and then print the Coefficients
How to Create a Linear Regression and Display it
In this example, random data is displayed in a plot. A linear regression model is then created against the data, and an estimated regression line is finally displayed.
# Import the packages and classes needed for this example: import numpy as np import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression # Create random data with numpy, and plot it with matplotlib: rnstate = np.random.RandomState(1) x = 10 * rnstate.rand(50) y = 2 * x  5 + rnstate.randn(50) plt.scatter(x, y); plt.show() # Create a linear regression model based the positioning of the data and Intercept, and predict a Best Fit: model = LinearRegression(fit_intercept=True) model.fit(x[:, np.newaxis], y) xfit = np.linspace(0, 10, 1000) yfit = model.predict(xfit[:, np.newaxis]) # Plot the estimated linear regression line with matplotlib: plt.scatter(x, y) plt.plot(xfit, yfit); plt.show()
Watch how to create a Linear Regression and display it in a Plot
Regression vs Classification
The main difference between regression and classification is that the output variable in regression is continuous, while the output for classification is discrete. Regression predicts quantity; classification predicts labels.
For information about classification, refer to: How to Classify Data in Python
The following tutorials will provide you with stepbystep instructions on how to work with machine learning Python packages:
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