Regression Visualizers

Regression models attempt to predict a target in a continuous space. Regressor score visualizers display the instances in model space to better understand how the model is making predictions. We currently have implemented three regressor evaluations:

• Residuals Plot: plot the difference between the expected and actual values
• Prediction Error Plot: plot the expected vs. actual values in model space
• Alpha Selection: visual tuning of regularization hyperparameters

Estimator score visualizers wrap Scikit-Learn estimators and expose the Estimator API such that they have fit(), predict(), and score() methods that call the appropriate estimator methods under the hood. Score visualizers can wrap an estimator and be passed in as the final step in a Pipeline or VisualPipeline.

# Regression Evaluation Imports

from sklearn.linear_model import Ridge, Lasso
from sklearn.model_selection import train_test_split

from yellowbrick.regressor import PredictionError, ResidualsPlot
from yellowbrick.regressor.alphas import AlphaSelection

• Residuals Plot
  • API Reference
• Prediction Error Plot
  • API Reference
• Alpha Selection
  • API Reference