What is the effect of using an appropriate training dataset?

Using an appropriate training dataset is crucial for effective model building in predictive analytics. When the training dataset is relevant and well-structured, it provides the model with high-quality information and patterns that it can learn from. This enhances the model's ability to understand relationships within the data, improving its predictive power and generalization capabilities.

A well-chosen training dataset allows the model to identify trends, recognize anomalies, and make better-informed predictions when applied to new or unseen data. Therefore, the direct correlation between a properly constructed training dataset and enhanced model performance is what makes this choice the most suitable answer.

In contrast, other options imply negative effects or outcomes that do not align with best practices in predictive analytics. For instance, increasing data volume could be misleading without ensuring quality and relevance. Similarly, reducing prediction accuracy and inhibiting model training directly contradict the intent and outcome of utilizing an appropriate training dataset.