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