Regressiontype

Regressiontype is a label used in statistical software and data schemas to indicate the family or category of regression model applied to predict an outcome. It is not a standard statistical term on its own, but it commonly appears as a field in configurations, metadata, or code templates to request or document the regression approach being used.

Common values in practice include linear, ridge, lasso, elastic-net, polynomial, spline, robust (M-estimation), quantile regression, and various generalized linear model families such as logistic (for binary outcomes), Poisson (count data), and gamma models. Nonparametric and semi-parametric options like LOESS or generalized additive models may also be included as regression-type candidates in some interfaces, though with different estimation semantics.

Usage and data pipelines: In automated machine learning workflows or model registries, regressiontype guides model creation, feature handling, and evaluation. Some frameworks use the exact string “regressionType” or “regression_type” in JSON or YAML to select a preset estimator or route to a modeling class; others store the type as metadata within a project or model artifact.

Implications: The choice of regressiontype affects statistical assumptions (such as linearity and error distribution), interpretability, and performance. It also influences data requirements and regularization strategies, and determines which evaluation metrics are appropriate (for example, RMSE for continuous outcomes or classification metrics for logistic-type models).

See also: Regression, Linear regression, Logistic regression, Ridge regression, Lasso, Elastic net, Quantile regression, Generalized linear models, Nonparametric regression.