Introduction Machine learning models, especially deep neural networks and ensemble methods, have far surpassed traditional linear models in predictive accuracy. However, their highly non-linear an...

With the widespread application of machine learning models, model interpretability has become a key issue. Traditional feature importance methods such as Permutation Importance and gradient-based m...

When building machine learning models, we not only pursue excellent predictive performance but also seek to understand the logic behind model decisions. Permutation Feature Importance (PFI) is a po...

When we talk about “linear regression,” what typically comes to mind is Ordinary Least Squares (OLS)—that “best fit” line through data points. OLS is simple and intuitive, but it gives a single, de...

In machine learning regression tasks, we’re always searching for the perfect loss function to guide model learning. The two most common choices are Mean Squared Error (MSE, L2 Loss) and Mean Absolu...