For regression tasks, Yellowbrick provides a that shows:
visualizer.fit(X_train, y_train) # Fits model AND prepares viz visualizer.score(X_test, y_test) # Scores and generates plot visualizer.show() # Renders the figure yellowbrick analyst tool
: Bengfort, B., & Bilbro, R. (2019). Yellowbrick: Visualizing the Scikit-Learn Model Selection Process. Journal of Open Source Software , 4(35), 1075. 4. Supporting Your Narrative For regression tasks, Yellowbrick provides a that shows:
Every time you train a model, ask yourself: Did I check the residual distribution? The learning curve? The feature correlation? Journal of Open Source Software , 4(35), 1075
When it comes to Model Selection, Yellowbrick shines by making complex metrics digestible. For classification tasks, you can instantly generate Confusion Matrices, ROC/AUC curves, and Precision-Recall curves with just a few lines of code. Unlike static plots generated by other libraries, Yellowbrick’s output is designed to highlight where a model is failing, such as identifying specific classes that are being misclassified.
: Published in the Journal of Open Source Software (JOSS) .