Variable Selection

Variable Selection is a statistical and machine learning method aimed at identifying the subset of candidate variables that contribute most to the predictive power of a model. Its primary goal is to enhance the interpretability and predictive accuracy of the model while reducing the risk of overfitting and computational costs. By optimizing the combination of variables, Variable Selection can improve the generalization and stability of the model, and it is widely applied in data mining, bioinformatics, financial analysis, and other fields, making it crucial for building efficient and reliable predictive models.