get_partial_dependence
Analyze how a single feature influences model predictions across its range, revealing average and individual effects through partial dependence plots and ICE visualizations.
Instructions
Show how a single feature affects predictions across its range.
Returns a narrative describing the relationship between the feature
and the model's predicted probability. Optionally includes a PDP + ICE
plot (model-agnostic visualization, not SHAP-based).
PDP (bold line) shows the average effect. ICE (gray lines) show individual
sample effects, revealing heterogeneity the average hides.
Args:
model_id: ID of a registered model (e.g., "gbc_lubricant_quality").
feature_name: Name of the feature to analyze (e.g., "mean radius").
include_plot: If True, include a PDP+ICE plot as base64 PNG (default: True).Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| model_id | Yes | ||
| feature_name | Yes | ||
| include_plot | No |