AI ModelClassical ML

Regression baseline models

Regression baseline models are simple predictive models used as reference points for evaluating more complex regression algorithms. They typically include strategies like predicting the mean, median, or last observed value of the target variable, helping practitioners determine whether sophisticated models provide meaningful improvements. Baselines matter because they prevent overfitting to noise and ensure that added model complexity is justified by real performance gains.

by N/A (general modeling concept; commonly implemented in libraries like scikit-learn)Academic

Key Features

  • •Extremely simple to implement and interpret (e.g., mean, median, or constant prediction)
  • •Provide a performance floor for comparing advanced regression models
  • •Low computational cost for training and inference
  • •Robust to small datasets and noisy features when used as sanity checks
  • •Model-agnostic: applicable to any regression problem regardless of domain

Pricing

OpenSource

Baseline regression strategies are conceptual techniques and are free to use; many open-source libraries (e.g., scikit-learn) provide built-in baseline regressors under permissive licenses.

Alternatives

Linear RegressionRandom Forest RegressorGradient Boosting Regressors (e.g., XGBoost, LightGBM)k-Nearest Neighbors Regressor

Industries Using Regression baseline models

agriculture1

Often Used With

Support Vector Regression (1)Time-Series DB (1)

Quick Stats

1
Total Use Cases
1
Industries
2
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