Model comparison

Model comparison involves evaluating different predictive models to determine which one best explains or predicts data. It’s like testing various tools to see which works most effectively for a task. We compare models based on criteria such as accuracy, simplicity, and how well they generalize to new data. This helps ensure we select the most appropriate model for making reliable decisions or predictions, balancing performance with complexity. Essentially, model comparison guides us to choose the most suitable approach among alternatives by assessing their strengths and weaknesses objectively.