Choosing between datasets when comparing models depends on three key factors: the problem domain, dataset quality, and practical constraints. First, ensure the dataset aligns with your specific task. For example, if you’re testing image classification models, MNIST (handwritten digits) and CIFAR-10 (small object images) serve different purposes: MNIST is simpler and useful for basic validation, while CIFAR-10 introduces color and texture complexity. A medical imaging model, however, would require domain-specific datasets like CheXNet (chest X-rays) to reflect real-world scenarios. Using unrelated datasets can lead to misleading performance metrics because models trained on general-purpose data often fail to generalize to niche tasks.
Next, evaluate dataset quality. Check for issues like noise, missing values, or biases. For instance, a sentiment analysis dataset with mislabeled reviews (e.g., positive comments marked as negative) could skew model accuracy. Tools like Pandas Profiling or manual sampling help identify these issues. Additionally, consider class balance: a facial recognition dataset with 90% images of one ethnicity will bias results. Preprocessing steps like normalization or augmentation can mitigate some problems, but the base dataset must still represent the problem space. For example, training a self-driving car model on synthetic data alone might not account for real-world lighting or weather variations, limiting practical utility.
Finally, factor in practical constraints like dataset size, licensing, and computational costs. Large datasets like ImageNet (14 million images) require significant storage and training time, which may not be feasible for small teams. Smaller, curated datasets like Fashion-MNIST (70,000 images) are easier to iterate on. Licensing is critical for compliance: datasets with restrictive licenses (e.g., some commercial image collections) may limit deployment options. Reproducibility also matters: using standardized splits (e.g., 80/20 train-test) or benchmarks like GLUE for NLP ensures fair comparisons. For example, comparing two NLP models on different Twitter sentiment datasets could hide performance gaps due to variations in slang or topic distribution. Always document your dataset choices to enable others to validate your results.