Feature selection is usually necessary in machine learning, as it identifies the optimal relevant features and removes irrelevant ones. The imbalance between the dimensions of the feature space and the data points in the space can negatively affect the performance of machine learning models. Ineffective descriptors with constant, reduplicative and/or missing values are first removed. The remaining descriptors are then screened by the Pearson correlation analysis, implemented by the psych R package, which guarantee a low correlation coefficient for the chosen descriptors.