A prediction error plot shows the actual targets from the dataset against the predicted values generated by our model. This allows us to see how much variance is in the model. Data scientists can diagnose regression models using this plot by comparing against the 45 degree line, where the prediction exactly matches the model.
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from sklearn.linear_model import Lasso from sklearn.model_selection import train_test_split
from yellowbrick.datasets import load_concrete from yellowbrick.regressor import PredictionError
# Load a regression dataset X, y = load_concrete()
# Create the train and test data X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Instantiate the linear model and visualizer model = Lasso() visualizer = PredictionError(model)
visualizer.fit(X_train, y_train) # Fit the training data to the visualizer visualizer.score(X_test, y_test) # Evaluate the model on the test data visualizer.show() # Finalize and render the figure
The same functionality can be achieved with the associated quick method prediction_error. This method will build the PredictionError object with the associated arguments, fit it, then (optionally) immediately show the visualization.
from sklearn.linear_model import Lasso from sklearn.model_selection import train_test_split
from yellowbrick.datasets import load_concrete from yellowbrick.regressor import prediction_error
# Load a regression dataset X, y = load_concrete()
# Create the train and test data X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Instantiate the linear model and visualizer model = Lasso() visualizer = prediction_error(model, X_train, y_train, X_test, y_test)
yellowbrick.regressor.prediction_error