This article explains how to evaluate machine learning model performance using Amazon SageMaker Canvas advanced metrics, focusing on customer churn prediction.

• SageMaker Canvas enables business analysts to build ML models without coding expertise
• Confusion matrix visualizes correct and incorrect predictions across categories
• Accuracy measures percentage of correct predictions but can be misleading with imbalanced data
• Precision, recall, and F1 score provide balanced evaluation of model performance
• AUC metric evaluates binary classification ability across all classification thresholds
• Data-centric approach improves performance through data preparation and feature engineering
• Model-centric approach uses AutoML, hyperparameter tuning, and algorithm optimization
• Feature importance analysis identifies which columns most impact predictions
• Standard build process tests hundreds of candidate pipelines for optimal accuracy

SageMaker Canvas empowers business analysts to build, evaluate, and improve ML models using visual tools and advanced metrics without requiring data science expertise.