Model B, because a higher R-squared means it explains more variance in the data

Model A, because a lower MAE indicates better prediction accuracy on average

Model B, because higher R-squared always indicates a better model

Neither, because both models have significant trade-offs and need further tuning

High computational requirements can increase costs, but it also provides an opportunity to invest in more energy-efficient hardware.

Data privacy concerns limit the availability of training data, but this also drives the development of synthetic data generation.

Model interpretability is difficult, but it can also lead to advancements in explainable AI.

Bias in the training data can lead to unfair or harmful outputs, but it also presents an opportunity to develop more ethical AI.

Model A, because it has a lower R²

Model B, because it has a higher R²

Model A, because a lower MSE always indicates a better model

Model B, because it has a higher MSE