Which practice can significantly enhance the quality of a training dataset?

Augmenting data through various transformations is a practice that enhances the quality of a training dataset by increasing its diversity and robustness. Data augmentation involves creating new training examples by slightly modifying existing ones, which can include techniques like rotation, scaling, flipping, changing brightness, or applying noise. This is particularly beneficial in scenarios where the amount of available data is limited, as it helps to artificially expand the dataset.

Through augmentation, models can learn to generalize better because they are exposed to a wider range of possible inputs, which prevents overfitting to the original dataset. The model becomes more capable of handling variations and noise in real-world data, leading to improved performance in subsequent evaluations.

On the other hand, using obsolete data may lead to poor model performance because the information may no longer be relevant or representative of current trends. Minimizing dataset size can limit the amount of information the model can learn from, potentially leading to lower accuracy and poor generalization. Excluding outliers without proper analysis could result in losing potentially valuable information, as outliers can sometimes provide insight into important phenomena or edge cases.