Baselines

In ML, baselines serve as reference models that provide a benchmark for evaluating the performance of more complex models. Baseline models are typically simple and make minimal assumptions about the data.

They provide a starting point for model development and evaluation. They are relatively simple models that establish a minimum level of performance against which other models can be compared. Baselines are useful for assessing whether a more complex model provides significant improvements over a simple reference point.

Baseline models serve several purposes, including the following:

• Performance evaluation: They provide a baseline against which the performance of more complex models can be compared.

• Model complexity assessment: Comparing a complex model to a baseline helps determine if the additional complexity is justified by the performance gain.

• Sanity check: Baselines allow us to verify if our more complex models are learning meaningful patterns in the data. ...