In the context of artificial intelligence (AI) and machine learning (ML), a “baseline” is a reference point or standard that is used to evaluate and compare the performance of different models, algorithms, or techniques. Think of it as a starting line in a race: before you can measure improvement, you need something to improve upon. Baselines help researchers and practitioners determine whether their new approach actually adds value or just matches what is already possible with simpler or established methods.
A baseline can take many forms, depending on the task. In a classification problem, a baseline might be a model that always predicts the most frequent class in the dataset. For a regression problem, it could be a model that always outputs the average target value. Baselines are not meant to be sophisticated or state-of-the-art; their job is to set a minimum standard of performance.
In the world of AI research, using a baseline is essential for fair and transparent evaluation. If a new model only slightly outperforms a strong baseline, its real-world impact might be limited. On the other hand, if it easily beats a weak baseline, the results could be misleading. That’s why it’s common to compare against multiple baselines, including both simple ones and more advanced models, to get a clear picture of progress.
Baselines are also useful in practical machine learning workflows. When building a new system, data scientists often start with a baseline model because it is quick to implement and offers a first glimpse into the difficulty of the problem. If a simple baseline achieves high performance, the task might be easy or the data might be imbalanced. If the baseline performs poorly, it suggests there is room for improvement and justifies building more complex models.
In addition to classic baselines, there are also “random” baselines, where predictions are made by chance, and “oracle” baselines, where predictions are made using some form of idealized information (often unattainable in practice). These help to bracket possible performance: random baselines indicate the lower bound and oracles the upper bound.
Sometimes, papers or benchmarks will refer to a “strong baseline.” This means the reference model is already quite competitive—maybe a previous state-of-the-art approach or a widely used algorithm in the field. Beating a strong baseline is much more meaningful than beating a naive one.
In summary, a baseline is a simple but crucial tool in AI and machine learning. It helps set expectations, guides model development, and ensures that improvements are meaningful. Whenever you see claims of improvement in AI, it’s always smart to check what baseline is being used for comparison.
