Yellowbrick Analysis Tool <95% TRUSTED>

Uses the same fit() / transform() / score() API. You can drop it into existing pipelines with minimal changes.

Here’s a helpful review of , a Python visualization library for machine learning diagnostics and analysis. yellowbrick analysis tool

Unsupervised learning is notoriously difficult to evaluate numerically. Uses the same fit() / transform() / score() API

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

Before modeling, it is crucial to understand the data structure.