Backward Selection

Backward Selection is a statistical method used to choose the best variables in a model. It starts with all available variables and gradually removes the least significant ones, one at a time. After each removal, the model is assessed to see how well it performs. This process continues until only the most important variables remain, ensuring a simpler model that still effectively explains the data. By focusing on the most relevant factors, Backward Selection helps make predictions clearer and more reliable without unnecessary complexity.