Evaluate the junior engineer's proposal from the case study below. Is this a sound strategy for improving the system? Justify your reasoning.

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The performance gains from output ensembling are not limitless. As the number of models in an ensemble grows, a point of diminishing returns is often reached where adding more models provides only marginal improvements in output quality, or none at all.

Diminishing Returns in Output Ensembling

A development team is creating an ensemble to improve the accuracy of a text summarization model. They measure the quality score of the summaries as they increase the number of models in the ensemble, with the following results:

| Number of Models | Quality Score |
|------------------|---------------|
| 1                | 78.0          |
| 3                | 84.0          |
| 5                | 86.5          |
| 7                | 87.5          |
| 9                | 87.7          |

Based on this data, which of the following conclusions is the most accurate interpretation of the ensemble's performance?

A machine learning team has an ensemble of 5 models for a critical task. They observe that each new model added to the ensemble provides a smaller accuracy boost than the last. They are now considering two options for their next development cycle, both requiring similar engineering effort:

*   **Option 1:** Add 5 more models to the ensemble, which is projected to increase overall accuracy by 0.2% and significantly increase computational costs.
*   **Option 2:** Focus on improving the quality of the training data for the existing 5 models, which is projected to increase overall accuracy by 1.5% without increasing computational costs.

Which option should the team choose? Justify your recommendation by explaining the underlying principle that makes one option more efficient than the other in this scenario.

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