Baseline Model

Baseline models serve as crucial points of comparison in evaluating the performance of new machine learning models across diverse fields, from healthcare and natural language processing to weather forecasting and image analysis. Current research emphasizes the need for stronger, more appropriate baselines, often focusing on established statistical methods or simpler architectures like linear models and Markov models, to accurately gauge the true advancements offered by novel approaches. The development and use of robust baselines are essential for ensuring the reproducibility and reliability of research findings, ultimately improving the validity and impact of machine learning applications in various domains.