Penalized Regression Models for GLMs and Cox Proportional Hazards

A collection for fitting penalized regression models with L1 and L2 penalties. The models support linear, logistic, and Poisson regression, as well as the Cox Proportional Hazards model, and include cross-validation routines for tuning parameter optimization. The dataset is associated with work by author Jelle Goeman and is hosted on PapersWithCode.

Use Cases

• Benchmarking Lasso (L1) and Ridge (L2) regularization techniques based on the described penalty structures.
• Training models for survival analysis using the described Cox Proportional Hazards model implementation.
• Optimizing tuning parameters for regression models using the described cross-validation routines.
• Comparing model performance across linear, logistic, and Poisson regression types as mentioned in the description.

Strengths

• Supports multiple regression models: linear, logistic, Poisson, and Cox Proportional Hazards.
• Includes cross-validation routines for tuning parameter optimization.
• Allows combination of L1 (lasso, fused lasso) and L2 (ridge) penalties with a positivity constraint.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

PapersWithCode

Collection Method

Likely contains code and data associated with the described penalized estimation methods.

Time Range

Temporal coverage is unknown.

Freshness

Last updated date is unknown.

Geography

Spatial coverage is unknown.