Xlowrelated

Xlowrelated is a term used in statistical analysis and machine learning to describe a set of features or variables whose pairwise correlation coefficients are below a predetermined low threshold. It is commonly employed during feature selection to minimize redundancy among predictors while retaining sufficient explanatory power. The practice originates from early work on principal component analysis, where low correlation among variables was sought to avoid multicollinearity. In contemporary applications, Xlowrelated criteria are often implemented algorithmically, for example by iteratively removing the variable with the highest average correlation until all remaining pairs fall below the chosen threshold, typically between 0.2 and 0.3. This approach is especially useful in high‑dimensional data sets such as genomic studies, where the number of covariates can far exceed the number of observations. While reducing multicollinearity can improve the stability of parameter estimates, overly aggressive application of the Xlowrelated rule may discard informative features that are only moderately correlated. Consequently, practitioners often balance Xlowrelated filtering with domain knowledge and cross‑validation to ensure optimal model performance.