Variable Selection

Variable selection is the process of identifying which factors (or variables) in a dataset are most important for understanding a particular outcome. In research or data analysis, many variables can influence results, but not all are equally relevant. By selecting only the significant ones, we can create a clearer model that reduces complexity, improves accuracy, and enhances interpretability. This practice helps avoid overfitting, where a model is too tailored to specific data and performs poorly on new information, ultimately leading to better predictions and insights.