📄 中文摘要
训练机器学习模型以预测二元结果（如欺诈与否、流失与留存、疾病与健康）后，数据科学家面临的关键问题是模型的有效性。评估是解决这一问题的关键。然而，许多人在评估时仅停留在准确率上，草率宣布成功并投入生产，结果导致模型在实际应用中表现不佳，因为忽视了数据或用例中的重要因素。该指南提供了全面的评估工具，包括各种指标、曲线及其背后的思考，帮助读者明确应该测量什么及其原因。
如何评估二元分类器：完整指南
出处: How to Evaluate a Binary Classifier: A Complete Guide
发布: 2026年3月29日
📄 English Summary
How to Evaluate a Binary Classifier: A Complete Guide
After training a machine learning model to predict binary outcomes such as fraud detection, customer churn, or health status, data scientists face a critical question: Is the model effective? Evaluation plays a crucial role in addressing this question. However, many practitioners stop at accuracy, prematurely declaring success and deploying the model, only to find it underperforms in production due to overlooked aspects of the data or use case. This guide provides a comprehensive evaluation toolkit, including metrics, curves, and the reasoning behind each, equipping readers with the knowledge of what to measure and why.