Yellowbrick | Analysis Tool ((hot))

Unsupervised learning is notoriously difficult to evaluate numerically.

model = RandomForestClassifier() visualizer = ClassificationReport(model, support=True) visualizer.fit(X_train, y_train) visualizer.score(X_test, y_test) visualizer.show()

Yellowbrick’s API is designed to feel familiar to Scikit-Learn users. It introduces the concept of a Visualizer —an estimator object that learns from data to produce a visual summary. The workflow mirrors the standard Scikit-Learn process: