Modelidentificatie
Modelidentificatie is the process of determining which statistical or mathematical model best represents a given set of data. This involves evaluating various candidate models and selecting the one that provides the most accurate and parsimonious explanation of the observed patterns. The goal is to find a model that not only fits the current data well but also generalizes effectively to new, unseen data.
Several criteria are used in modelidentificatie. Goodness-of-fit measures, such as R-squared or residual sum of squares,
Cross-validation is another crucial technique. It involves splitting the data into training and testing sets. A