In PLS regression, what is the main focus of the analysis?

In Partial Least Squares (PLS) regression, the primary focus is on accounting for variations in both inputs and outputs. This technique seeks to establish a relationship between two sets of variables—input predictors and output responses—by projecting them into a new space where their correlations can be maximized. PLS regression is particularly useful when dealing with highly collinear data or when the number of input variables exceeds the number of observations.

By simultaneously considering the variations in both the predictive and response variables, PLS regression can effectively identify the latent structures that predict output responses while capturing the complexity of the data. This dual focus allows PLS to produce more reliable and interpretable results compared to methods that only consider one aspect of the data at a time. This is especially relevant in scenarios where traditional regression methods may struggle due to multicollinearity or overfitting issues.

Other options do not encapsulate the core objective of PLS regression. For instance, while minimizing input dimensions may be an outcome of the analysis, it is not the primary goal. The same applies to maximizing outputs or the diversity of input data, which do not directly reflect the method's focus on the relationship between inputs and outputs.