Model Selection

Model selection refers to the process of choosing the model that best approximates the observed data and captures its underlying patterns from a set of candidate models. This process involves defining criteria for model selection to balance goodness of fit with the model's generalization ability or complexity, ensuring that the selected model not only fits the existing data well but also has strong predictive power and stability. Model selection is of significant application value in machine learning and statistical analysis, effectively enhancing the performance and reliability of models.