Final answer:
The deployment phase of building a predictive model involves using the model in real-life applications to make predictions and monitor its performance. It integrates the model into current systems to provide quick predictions, although there's a risk of inaccuracy due to various factors.
Step-by-step explanation:
When building a predictive model, the goal of the deployment phase is C: To use the model in real-life predictions, monitoring the error rate and accuracy. This phase is where the model, now fully developed, tested, and evaluated, is implemented into a production environment to solve the problem it was designed to address. It involves integrating the model into existing systems and processes and ensuring it operates correctly with new and unseen data in a real-world context.
The advantage of using a model is that it often provides predictions quickly, which can be extremely valuable in applications where timeliness is critical. However, the disadvantage includes the potential for erroneous predictions, which can be costly and may stem from factors such as model overfitting, data quality issues, or changes in the underlying patterns that the model is attempting to predict.
Models exist as a form of hypothesis and are assessed by their accuracy, how well they represent the real world, limitations, and overall usefulness. The mathematical model should be thoroughly tested against relevant criteria to ensure that it has been constructed properly and adequately represents the science and mathematics it's based on.