A model baseline serves as a reference point for evaluating the performance of more complex machine learning models. It is typically a simple model that does not take the input features into account, allowing you to determine if your advanced model is genuinely learning from the data or merely performing at a level comparable to random or fixed predictions.

Creating a baseline model is essential because it helps identify potential issues with data quality or model design. If the baseline model performs poorly, it may indicate problems with the data that need to be addressed. Conversely, if the baseline outperforms the complex model, it suggests that the latter may require further tuning or that a simpler model could suffice for the task at hand ⁴⁵.

Common strategies for establishing a baseline include using statistical models, such as moving averages, or simpler versions of the target model, like a Decision Tree when planning to use a Random Forest ⁴. Tools like Sklearn can simplify the process of creating baseline models with functions like DummyClassifier and DummyRegressor ².