What must be verified during the deployment phase of a predictive model?

During the deployment phase of a predictive model, it is crucial to verify that the model still meets performance targets. After a model has been developed and trained, it is typically evaluated against metrics such as accuracy, precision, recall, or other relevant measures. However, once the model is deployed in a real-world environment, changes in data patterns, business processes, or external factors may occur, potentially affecting its performance.

Verifying that the model continues to meet its targets ensures that it remains effective and useful for the business objectives it was designed to address. This involves monitoring the model's predictions against actual outcomes and adjusting the model if necessary to maintain its relevance.

The other options, while related to model development and understanding, do not pertain specifically to the critical aspect of verification during deployment. Generating an understanding of business data and preparing data for model creation are important in earlier phases of the modeling process. Similarly, computing the most accurate model is part of the initial model development and evaluation rather than deployment.