Regression & Modeling · TOOL GUIDE

Regularized Regression (Lasso/Ridge)

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Understand the method. Use it with confidence.

Regression with regularization to prevent overfitting. Quantly runs the method against your active analysis context and returns structured results for review and interpretation.

01

When to use it

Use this tool to quantify relationships, explain variation, or predict an outcome from one or more features.
Use it only when the selected variables and study design match the method's requirements.

02

How to use it

  1. Open the tool from the Quantly Tools panel.
  2. Confirm the active dataset and analysis session.
  3. Set Target Variable (Dependent) (Required): The numeric variable you want to predict.
  4. Set Feature Columns (Independent) (Required): The predictor variables.
  5. Set Regularization Type (Required): Set regularization type.
  6. Set Alpha (Regularization Strength) (Optional): Constant that multiplies the penalty terms. Higher alpha = more regularization. set 0 for OLS.
  7. Set Use Cross-Validation to find best Alpha (Optional): If checked, automatically finds the best alpha from a range of values.
  8. Set Categorical Encoding (Required): How to transform text variables for the model. One-Hot is usually safer for this model.
  9. Review optional or advanced settings and keep defaults unless your design requires a change.
  10. Run the tool, inspect warnings and diagnostics, then save or export the result with its assumptions.
03

How to interpret results

Review coefficients or feature effects, uncertainty, validation metrics, residual diagnostics, and leakage risk. Association is not causation, and performance should be evaluated on data not used for fitting.