Beyond XGBoost and SHAP: Unveiling true feature importance.

This paper outlines key machine learning principles, focusing on the use of XGBoost and SHAP values to assist researchers in avoiding analytical pitfalls. XGBoost builds models by incrementally adding decision trees, each addressing the errors of the previous one, which can result in inflated feature importance scores due to the method's emphasis on misclassified examples. While SHAP values provide a theoretically robust way to interpret predictions, their dependence on model structure and feature interactions can introduce biases. The lack of ground truth values complicates model evaluation, as biased feature importance can obscure real relationships with target variables. Ground truth values, representing the actual labels used in model training and validation, are crucial for improving predictive accuracy, serving as benchmarks for comparing model outcomes to true results. However, they do not ensure real associations between features and targets. Instead, they help gauge the model's effectiveness in achieving high accuracy. This paper underscores the necessity for researchers to recognize biases in feature importance and model evaluation, advocating for the use of rigorous statistical methods to enhance the reliability of analyses in machine learning research.