Calibration is a critical yet often overlooked aspect of risk prediction models in predictive analytics. Poorly calibrated models can lead to misleading and potentially harmful clinical decisions. This article emphasizes the importance of assessing and improving calibration during both model development and validation. Calibration refers to the agreement between predicted and observed outcomes. It is essential for ensuring that risk estimates are reliable and useful for clinical decision-making, even when discrimination (the ability to distinguish between outcomes) is moderate. Calibration issues can arise from various sources, including differences in patient populations, measurement errors, and overfitting of models. Overfitting occurs when models are too complex relative to the data, leading to poor generalization. Calibration can be assessed using various methods, including calibration curves and statistical tests like the Hosmer-Lemeshow test. However, the Hosmer-Lemeshow test is not recommended due to its limitations. To prevent poor calibration, it is important to use appropriate modeling strategies, ensure sufficient sample sizes, and avoid overfitting. Internal validation can help assess calibration-in-the-large, while external validation is crucial for evaluating model performance in new settings. When calibration is poor, updating the model can improve its accuracy. For example, recalibrating the model by adjusting intercepts or refitting the algorithm can enhance its performance. The article also highlights the importance of continuous monitoring and updating of predictive models, especially as population characteristics change over time. This is particularly relevant with the increasing use of flexible machine learning algorithms. The ultimate goal is to ensure that predictive analytics are accurate, reliable, and beneficial for shared decision-making and patient counseling.Calibration is a critical yet often overlooked aspect of risk prediction models in predictive analytics. Poorly calibrated models can lead to misleading and potentially harmful clinical decisions. This article emphasizes the importance of assessing and improving calibration during both model development and validation. Calibration refers to the agreement between predicted and observed outcomes. It is essential for ensuring that risk estimates are reliable and useful for clinical decision-making, even when discrimination (the ability to distinguish between outcomes) is moderate. Calibration issues can arise from various sources, including differences in patient populations, measurement errors, and overfitting of models. Overfitting occurs when models are too complex relative to the data, leading to poor generalization. Calibration can be assessed using various methods, including calibration curves and statistical tests like the Hosmer-Lemeshow test. However, the Hosmer-Lemeshow test is not recommended due to its limitations. To prevent poor calibration, it is important to use appropriate modeling strategies, ensure sufficient sample sizes, and avoid overfitting. Internal validation can help assess calibration-in-the-large, while external validation is crucial for evaluating model performance in new settings. When calibration is poor, updating the model can improve its accuracy. For example, recalibrating the model by adjusting intercepts or refitting the algorithm can enhance its performance. The article also highlights the importance of continuous monitoring and updating of predictive models, especially as population characteristics change over time. This is particularly relevant with the increasing use of flexible machine learning algorithms. The ultimate goal is to ensure that predictive analytics are accurate, reliable, and beneficial for shared decision-making and patient counseling.