A shap plot is a human-made visualization technique in machine learning interpretability that displays SHAP (SHapley Additive exPlanations) values to quantify how each feature contributes to a model's prediction for individual instances. The mechanism of persistence is software libraries (python-shap, R shap) that compute feature attributions using Shapley values from cooperative game theory and render them as bar charts, summary plots, dependence plots, or waterfalls. Parameters: (1) the attribution values (positive = pushes prediction up, negative = pushes down), (2) the feature names being attributed, (3) the model type (tree-based, linear, or any model with a TreeSHAP/KernelSHAP explainer). The plot enables both global feature importance (summary across all instances) and local interpretability (per-instance contribution breakdown). [formal: graphic | substrate: mind | horizon: a session | explicit: yes | epoch: 0.15]
Accepted ontology entry
shap plot
A shap plot is a human-made visualization technique in machine learning interpretability that displays SHAP (SHapley Additive exPlanations) values to quantify how each feature contributes to a model's prediction for individual instances. T…
Definition
Why it is in scope
A human-made visualization technique in machine learning interpretability: a plot that displays SHAP (SHapley Additive exPlanations) values to show how each feature contributes to a model's prediction for individual instances. Created by Scott Lundberg and Haruki Lee as part of the SHAP framework, these plots map feature importance and direction of influence in model outputs.
Names and aliases
- shap ploten · CANONICAL
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- cmsk6gz0j03yknobp6dik93doINSTANCE_OF →
A shap plot IS a specific kind of diagnostic plot — it diagnoses model behavior by visualizing feature importance and SHAP values. Per Law 9, a competent speaker would call a shap plot a diagnostic plot.
- cmsebunmy05543vv3xqux72jrSERVES →
SHAP plots are designed and maintained for the sake of feature importance: their purpose is to visualize, communicate, and make interpretable the importance scores of input features. For whose sake? Feature importance — the plot is the servant, the concept is the master. Law 8d: the servant points at the master.
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Record identity
- Created
- Aug 8, 2026, 9:47 AM UTC
- Content hash
- 2fdbe7af5aabbaf0c8219151c446984cc3553dc93365ce0b020e56356cdc59c5