Model Selection

Model selection is the process of choosing the best mathematical model to analyze data and make predictions. It involves comparing different models based on how well they fit the data and their ability to predict new data accurately. The goal is to find a balance: a model that is complex enough to capture important patterns but not so complex that it learns noise or random fluctuations. Good model selection improves decision-making and ensures the insights drawn are reliable. Techniques like cross-validation or information criteria help in objectively comparing models and selecting the most appropriate one for the data and the problem at hand.