______________ models (linear regression, logistic regression) are easy to interpret, but linear predictions might lead to prediction bias.

The choice of "Simple" models in this context is appropriate because these types of models, such as linear regression and logistic regression, are known for their straightforward interpretability. Simple models use linear relationships to make predictions, which means their parameters can be easily understood; for instance, the coefficients in a linear regression indicate the strength and direction of the relationship between each predictor and the dependent variable.

However, the concern with linear predictions leading to prediction bias arises from the assumption that relationships in the data are indeed linear. If the true relationship is more complex or non-linear, using a simple linear model may oversimplify the patterns in the data. This can result in systematic errors in predictions, as these models may not capture the underlying trends accurately. Hence, while simple models provide ease of interpretation and implementation, their assumptions about linearity can limit their effectiveness in certain scenarios, leading to bias in predictions.

In summary, the term "Simple" accurately reflects the nature of these models, emphasizing their interpretability, while also highlighting the potential for bias when applied to non-linear data.