Plotting you residuals for regression problems is crazy useful. This is another diagnostic plot that you can use to figure out if you did something incorrectly. Your residuals should look random, if they are not, you probably have an error in your model somewhere. I use the Seaborn residplot to plot all my residuals, the plot works really well with Scikit Learn models and Numpy arrays making it flexible.
%matplotlib inline %config InlineBackend.figure_format='retina' # Import modulse import matplotlib.pyplot as plt import seaborn as sns from sklearn import datasets from sklearn.linear_model import LinearRegression # load the diabetes datasets diabetes = datasets.load_diabetes() # Assigning targed and feature data X = diabetes.data y = diabetes.target # Calling and training the linear model reg = LinearRegression() reg.fit(X,y) # Predicteing target values with trained model pred_y = reg.predict(X)
# Plotting the residuals of y and pred_y sns.residplot(y,pred_y, color='#01B6B7') plt.title('Model Residuals') plt.xlabel('Obsevation #') plt.ylabel('Error');
Author: Kavi Sekhon