I Thought My AI Models Were 95% Accurate. Then I Looked Closer.
中文摘要
作者通过重建机器学习项目，探讨了为何不能盲信准确率，以及如何更严谨地评估模型性能与数据质量。
English Summary
Rebuilding two ML projects revealed the dangers of relying on surface accuracy, highlighting the importance of rigorous model evaluation and data quality.
原文节选
Why rebuilding two machine learning projects changed how I evaluate model performance and data quality. Continue reading on Medium »