Balanced Split

Balanced data splitting is crucial for reliable machine learning, particularly in addressing class imbalance where some categories are underrepresented. Research focuses on developing novel splitting strategies, such as those minimizing error rates in controlled trials or maximizing diversity within subgroups, and on evaluating the impact of data splits on model performance, for example, in natural language processing tasks like sentence splitting and parsing. These efforts aim to improve the accuracy and generalizability of machine learning models by mitigating biases introduced by imbalanced datasets, leading to more robust and reliable results across various applications.