I Wasn’t Trying to Find the Best Machine Learning Model. I Was Trying to Build One I Could Trust.
中文摘要
作者通过运费预测案例，探讨了如何通过处理杂乱数据、验证及特征可用性，构建比单纯追求高性能更值得信赖的机器学习模型。
English Summary
The author discusses building trustworthy machine learning models for freight-rate prediction, prioritizing data quality, validation, and feature availability over mere model performance optimization.
原文节选
What a freight-rate prediction problem taught me about messy data, validation, feature availability, and the decisions that happen before… Continue reading on Medium »