Forward Selection

Forward Selection is a step-by-step method used in building statistical models to identify the most important predictors. It starts with no variables and adds them one at a time, each time choosing the variable that improves the model the most. This process continues until adding more variables no longer significantly enhances the model's accuracy. Think of it as gradually building a team, carefully selecting the best members to improve overall performance without unnecessary complexity. It helps create efficient, effective models by focusing on the most impactful variables from the start.