Baselinemodeli

Baselinemodeli refers to simple, reference predictive models used as baselines against which more sophisticated models are compared. In predictive modeling, a baseline provides a minimal standard of performance that a developed model should surpass. Common forms include for classification: predicting the majority class or a random baseline; for regression: predicting the mean or median; for time series: using the last observed value as a forecast. Probabilistic baselines may predict the base rate of the positive class or a simple calibrated probability.

The baseline serves as a yardstick, helping to detect overfitting and to judge whether added model complexity is warranted. It also communicates what can reasonably be expected given the data and task, making it easier to interpret improvements.

When implementing baselinemodeli, practitioners choose a baseline that reflects the deployment setting and data generation process. Baselines should be evaluated using the same train-test split and metrics as the target models. Improvements over baselinemodeli are meaningful when the baseline is appropriate; otherwise, reported gains may be misleading.

Variants include domain-specific baselines, such as heuristics derived from domain knowledge, and simple ensemble baselines that combine several naive predictors. Baselinemodeli are commonly reported in academic papers, data competitions, and industry reports as a transparent starting point for model evaluation.

See also: baseline model, null model, dummy classifier, model evaluation.