Model Performance Assessment

Model performance assessment involves evaluating how well a predictive model accurately makes predictions or classifications. It uses specific metrics, like accuracy or error rates, to measure the model's effectiveness on data it hasn't seen before. This process helps determine if the model is reliable and generalizes well to real-world scenarios. By assessing performance, data scientists can identify strengths and weaknesses of the model, ensuring it provides meaningful and trustworthy results when applied to new data or decisions.