upweighting

Upweighting is the practice of assigning greater influence to certain observations, components, or terms within a statistical model or learning algorithm by granting them larger weights in estimation or optimization. The effect is to increase their contribution to the fitted values, predictions, or objective function relative to other elements.

In statistics and econometrics, weights often reflect sampling design or measurement reliability. Inverse-probability weights adjust estimates to be representative of a population when units are sampled with unequal probabilities; calibration or post-stratification weights can adjust for known margins. In regression, weighted least squares or generalized least squares use weights to account for heteroskedasticity or differential precision, giving more reliable observations more influence.

In machine learning and data science, upweighting is widely used to address class imbalance or to emphasize particularly informative instances. Loss functions may incorporate class weights that multiply the error for minority classes, or instance weights that emphasize certain examples. Techniques include weighted cross-entropy, focal loss, and explicit sample weighting during training.

Considerations and limitations: upweighting changes bias-variance tradeoffs and can introduce bias if weights misrepresent true importance; excessive weights can amplify noise or outliers. Weights should be chosen with the target estimand in mind, and sensitivity analyses are often recommended to assess robustness to weight choices.