<ul data-eligibleForWebStory="false"><li>Stratified sampling and cross-validation are crucial methods for fair and accurate machine learning, especially with imbalanced or high-stakes datasets.</li><li>Randomly splitting data may lead to biased models that ignore minority cases, highlighting the importance of proper data division for model success.</li><li>The author shares a personal experience where initial model success on paper did not translate to real-world tests, emphasizing the significance of effective data splitting.</li><li>Understanding stratified sampling and cross-validation can significantly improve model accuracy and fairness by preserving class distribution and enhancing training procedures.</li></ul>

Stratified Sampling & Cross-Validation: Keys to Fair, Accurate ML

Discover more