Which model selection method starts with a saturated model and removes inputs sequentially?

The backward model selection method begins with a saturated model, which includes all potential predictor variables in the model. After this initial model is established, the process involves systematically removing the least significant predictors one at a time. This method is particularly effective for identifying the most relevant variables while refining the model, as it allows the analyst to assess the impact of each variable on the overall model performance.

In backward selection, the focus is on simplifying the model by discarding variables that do not significantly contribute to the predictive ability of the model, thereby enhancing interpretability and reducing the risk of overfitting. This approach contrasts with other methods, such as forward selection, which starts with no predictors and adds them incrementally based on their significance. Stepwise selection combines both forward and backward methods, adding and removing variables based on their statistical significance, while exhaustive techniques evaluate all possible combinations of predictors, which can be computationally intensive.

Thus, understanding the mechanics of the backward selection method is crucial for effectively applying it in practical scenarios, particularly when working with complex datasets where the goal is to streamline the model without losing predictive power.