The absence of reported observations in the training data may lead to which type of bias?

When training a model, the absence of reported observations in the training data leads to what is known as reporting bias. Reporting bias occurs when there is a systematic error in the collection or presentation of data, specifically in how some outcomes or data points are ignored or unreported. This results in an incomplete representation of the true distribution of the data, which can significantly influence the model's performance and generalizability.

For instance, if certain groups or scenarios are underreported in the dataset, the model may fail to learn important patterns associated with those groups or scenarios, leading to potentially skewed or ineffective predictions. In contrast, the other bias types mentioned, such as selection bias, primarily relate to how samples are chosen rather than how data is reported or collected. Underfitting and overfitting are model performance issues tied to a model’s ability to learn from data, rather than the quality or completeness of the training dataset itself. Hence, reporting bias is the most suitable choice in the context of missing observations within the training data.