Which partitioning approach yields more stable predictive models?

The approach that yields more stable predictive models is one that allocates more data to training and less to validation. When more data is designated for training, the model has a greater opportunity to learn from a broader array of examples, leading to improved generalization on unseen data. A well-trained model tends to capture the underlying patterns and relationships in the data, which enhances its predictive performance.

Having a substantial training set allows for better tuning of the model's parameters and can lead to increased accuracy. It also helps in reducing overfitting, as the model can learn diverse characteristics of the data rather than simply memorizing a limited sample. Consequently, a strong training set lays the foundation for a model that is both robust and versatile, capable of performing well when applied to new data.

In contrast, if too much data is allocated to validation or testing, the training process might be compromised. The model would then be exposed to a limited number of examples, which could prevent it from developing a deep understanding of the data structure, negatively affecting its predictive stability.