Regression & Modeling · TOOL GUIDE

Multinomial Logistic Regression

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Understand the method. Use it with confidence.

Model outcome with more than two categories. 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 Dependent Variable (Y) (Required): The categorical outcome (3+ levels).
  4. Set Reference (Base) Category (Required): Category used as the baseline (must exist in the outcome).
  5. Set Independent Variables (X) (Required): One or more predictors. Categorical predictors will be one-hot encoded.
  6. Set Collapse Rare Outcome Classes (Optional): If checked, rare outcome classes are collapsed into 'Other' to stabilize the model.
  7. Set Minimum Count per Outcome Class (Optional): Attempt to keep only classes with at least this many rows (adaptive: tries this, then 2).
  8. Set Generate Confusion Matrix Plot (Optional): Visualize model performance with a heatmap.
  9. Set Generate Coefficient Plot (Optional): Visualize the model coefficients and their confidence intervals.
  10. Review optional or advanced settings and keep defaults unless your design requires a change.
  11. 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.