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ml-model-explainer skill

/ml-model-explainer

This skill explains ML model predictions using SHAP values, feature importance, and decision paths with clear visualizations.

npx playbooks add skill dkyazzentwatwa/chatgpt-skills --skill ml-model-explainer

Review the files below or copy the command above to add this skill to your agents.

Files (3)
SKILL.md
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---
name: ml-model-explainer
description: Explain ML model predictions using SHAP values, feature importance, and decision paths with visualizations.
---

# ML Model Explainer

Explain machine learning model predictions using SHAP and feature importance.

## Features

- **SHAP Values**: Explain individual predictions
- **Feature Importance**: Global feature rankings
- **Decision Paths**: Trace prediction logic
- **Visualizations**: Waterfall, force plots, summary plots
- **Multiple Models**: Support for tree-based, linear, neural networks
- **Batch Explanations**: Explain multiple predictions

## Quick Start

```python
from ml_model_explainer import MLModelExplainer

explainer = MLModelExplainer()
explainer.load_model(model, X_train)

# Explain single prediction
explanation = explainer.explain(X_test[0])
explainer.plot_waterfall('explanation.png')

# Feature importance
importance = explainer.feature_importance()
```

## CLI Usage

```bash
python ml_model_explainer.py --model model.pkl --data test.csv --output explanations/
```

## Dependencies

- shap>=0.42.0
- scikit-learn>=1.3.0
- pandas>=2.0.0
- numpy>=1.24.0
- matplotlib>=3.7.0

Overview

This skill explains machine learning model predictions using SHAP values, feature importance, and decision-path tracing, with built-in visualization support. It supports tree-based, linear, and neural models and produces both single-prediction and batch-level explanations for practical model introspection. The tool is intended for data scientists and engineers who need transparent, reproducible explanations for model outputs.

How this skill works

Load your trained model and representative training data; the skill fits SHAP explainers appropriate to the model type and computes local and global attribution scores. It exposes methods to extract per-instance SHAP values, aggregate feature importance, and trace decision paths for tree models. Visual helpers generate waterfall, force, and summary plots and can save figures for reporting or embedding in dashboards.

When to use it

  • Investigating why a model made a specific prediction for a given instance
  • Auditing feature influence and detecting data or model biases
  • Communicating model behavior to stakeholders with visual explanations
  • Comparing feature importance across different models or retrainings
  • Generating batch explanations for debugging or monitoring model drift

Best practices

  • Provide representative background data when creating SHAP explainers to get stable attributions
  • Use summary plots for global patterns and waterfall/force plots for single-instance narratives
  • Normalize or align feature preprocessing so explanations map to the features stakeholders know
  • Validate explanations across multiple instances and check consistency with domain knowledge
  • Persist explanation artifacts (plots, numeric attributions) for reproducibility and audits

Example use cases

  • Explain why a loan applicant was denied by showing top contributing features and their SHAP values
  • Compare feature importance before and after a model retrain to detect shifts
  • Produce per-customer explanation reports for a recommender system showing what drove a suggested item
  • Batch-explain a sample of predictions to detect systematic errors or fairness issues
  • Trace the decision path in a gradient boosted tree to show exact splits that led to a prediction

FAQ

Which model types are supported?

Tree-based models, linear models, and neural networks are supported via appropriate SHAP explainers; behavior differs slightly by model type.

Do I need special preprocessing to get correct explanations?

Explanations are most meaningful when you apply the same preprocessing used in training and provide matching background data to the explainer.