Which statement accurately describes the R-squared variable selection criteria in the Variable Selection node?

The R-squared variable selection criteria in the Variable Selection node focuses on assessing the strength of the relationship between input variables and the target variable by utilizing squared correlations. This approach is inherently related to the statistical technique of regression analysis. The R-squared value measures how much of the variability in the target variable can be explained by the input variables.

In this context, the criteria involve using stepwise regression, which systematically adds or removes predictors based on their statistical significance and contribution to the model's predictive power, particularly focusing on maximizing the R-squared value. This process helps in determining the most relevant inputs to retain in the model by evaluating their contributions through squared correlations, leading to more efficient and interpretable models.

This method is particularly useful in scenarios where many input variables exist, allowing practitioners to refine model selection by retaining only those inputs that add meaningful explanatory power. This is why the selected answer accurately summarizes the function of R-squared in the Variable Selection node.