What is primarily reduced in a model due to negative transfer learning?

Negative transfer learning occurs when a model trained on one task performs worse on a new but related task than it would have if it were trained solely on that new task. This scenario usually arises when the source domain data fundamentally differs in characteristics or context from the target domain data. As a result, the knowledge acquired from the source domain can confuse the learning process for the target task, leading to less effective learning.

Model accuracy is primarily affected in this context because the model's performance on the target task deteriorates due to the misleading information that is not relevant to the new task. Consequently, the ability of the model to make accurate predictions is compromised, leading to lower accuracy metrics.

The other options primarily focus on aspects of model efficiency or architecture rather than performance outputs. For instance, model complexity refers to the intricacy of the model architecture itself but does not directly correlate to how well the model performs on specific tasks in the context of negative transfer. Similarly, model training time and size are impacted by different factors not inherently tied to the negative transfer dynamics. Negative transfer is a phenomenon more about the relevance and appropriateness of the knowledge transferred, ultimately reflecting on the accuracy of the model’s predictions.