A new data-science case study compares XGBoost with logistic regression on a 358-match prediction problem and finds the simpler model performed better under cross-validation. The result is a practical reminder that more flexible models are not automatically better on limited data.
The core issue is bias and variance. XGBoost can model more complex patterns, but that flexibility can become a liability when the dataset is too small or noisy to support it.
For machine-learning teams, the lesson is familiar but easy to ignore: start with strong baselines, validate carefully and reach for larger models only when the evidence supports the added complexity.