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Introduction to Data Science with Python

Regression Plots

Explore how to use Seaborn's lmplot function to visualize linear and logistic regression models effectively. Learn to customize plots with hue, markers, palettes, grids, and control plot size and aspects. Understand how to interpret regression fits and improve data insights through better visualization techniques.

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Seaborn has many built-in capabilities for regression plots. The regression plots in seaborn are primarily intended to add a visual guide that helps to emphasize patterns in a dataset during exploratory data analysis. Let’s learn about the lmplot() method in this lesson.

First things first, we need to import seaborn and matplotlib.

C++
import seaborn as sns
import matplotlib.pyplot as plt
tips = sns.load_dataset('tips')
print(tips.head())

The lmplot() method

The lmplot() method allows us to display linear models with seaborn. The method plots data, as well as a regression model, across a FacetGrid. This function actually combines regplot() and FacetGrid.

Understanding the difference between regplot() and lmplot() can be a bit tricky. In fact, they are closely related, as lmplot() uses regplot() ...