Explaining Machine Learning Models

Name: Explaining Machine Learning Models
Author: jeremylongshore

jeremylongshore/claude-code-plugins-plus-skills

39 installs
2.6k repo stars
Updated August 5, 2026
jeremylongshore/claude-code-plugins-plus-skills

Interpret machine learning predictions using SHAP, LIME, and feature-importance analysis to explain why a model made a decision.

About

Applies SHAP, LIME, and feature-importance analysis to explain individual model predictions and overall behavior. A developer uses it to debug models or communicate model insights to stakeholders.

Uses SHAP and LIME for prediction explanations
Identifies most influential features driving outputs

Explaining Machine Learning Models by the numbers

39 all-time installs (skills.sh)
Ranked #1,006 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
Data as of Aug 5, 2026 (Skillselion catalog sync)

npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill explaining-machine-learning-models

Add your badge

Show developers this skill is listed on Skillselion. Paste this into your README.

[![Listed on Skillselion](https://skillselion.com/badge/skills/jeremylongshore/claude-code-plugins-plus-skills/explaining-machine-learning-models.svg)](https://skillselion.com/skills/jeremylongshore/claude-code-plugins-plus-skills/explaining-machine-learning-models)

Installs	39
repo stars	★ 2.6k
Last updated	August 5, 2026
Repository	jeremylongshore/claude-code-plugins-plus-skills ↗

What it does

Interpret machine learning predictions using SHAP, LIME, and feature-importance analysis to explain why a model made a decision.

Files

SKILL.mdMarkdownGitHub ↗

Model Explainability Tool

Interpret machine learning model predictions using SHAP, LIME, and feature importance analysis to explain model behavior.

Overview

This skill empowers Claude to analyze and explain machine learning models. It helps users understand why a model makes certain predictions, identify the most influential features, and gain insights into the model's overall behavior.

How It Works

1. Analyze Context: Claude analyzes the user's request and the available model data. 2. Select Explanation Technique: Claude chooses the most appropriate explanation technique (e.g., SHAP, LIME) based on the model type and the user's needs. 3. Generate Explanations: Claude uses the selected technique to generate explanations for model predictions. 4. Present Results: Claude presents the explanations in a clear and concise format, highlighting key insights and feature importances.

When to Use This Skill

This skill activates when you need to:

Understand why a machine learning model made a specific prediction.
Identify the most important features influencing a model's output.
Debug model performance issues by identifying unexpected feature interactions.
Communicate model insights to non-technical stakeholders.
Ensure fairness and transparency in model predictions.

Examples

Example 1: Understanding Loan Application Decisions

User request: "Explain why this loan application was rejected."

The skill will: 1. Analyze the loan application data and the model's prediction. 2. Calculate SHAP values to determine the contribution of each feature to the rejection decision. 3. Present the results, highlighting the features that most strongly influenced the outcome, such as credit score or debt-to-income ratio.

Example 2: Identifying Key Factors in Customer Churn

User request: "Interpret the customer churn model and identify the most important factors."

The skill will: 1. Analyze the customer churn model and its predictions. 2. Use LIME to generate local explanations for individual customer churn predictions. 3. Aggregate the LIME explanations to identify the most important features driving churn, such as customer tenure or service usage.

Best Practices

Model Type: Choose the explanation technique that is most appropriate for the model type (e.g., tree-based models, neural networks).
Data Preprocessing: Ensure that the data used for explanation is properly preprocessed and aligned with the model's input format.
Visualization: Use visualizations to effectively communicate model insights and feature importances.

Integration

This skill integrates with other data analysis and visualization plugins to provide a comprehensive model understanding workflow. It can be used in conjunction with data cleaning and preprocessing plugins to ensure data quality and with visualization tools to present the explanation results in an informative way.

Prerequisites

Appropriate file access permissions
Required dependencies installed

Instructions

1. Invoke this skill when the trigger conditions are met 2. Provide necessary context and parameters 3. Review the generated output 4. Apply modifications as needed

Output

The skill produces structured output relevant to the task.

Error Handling

Invalid input: Prompts for correction
Missing dependencies: Lists required components
Permission errors: Suggests remediation steps

Resources

Project documentation
Related skills and commands

{
  "_comment": "Example JSON output for a model explanation. This is a template for the model-explainability-tool plugin.",
  "model_id": "model_v3.2",
  "model_type": "Classification",
  "dataset_used": "customer_churn_dataset.csv",
  "explanation_type": "SHAP",
  "explanation_timestamp": "2024-01-26T10:30:00Z",
  "global_explanation": {
    "_comment": "Global feature importance ranking.",
    "feature_importance": [
      {
        "feature": "contract_length",
        "importance_score": 0.35,
        "description": "Length of the customer's contract (e.g., monthly, yearly)."
      },
      {
        "feature": "monthly_charges",
        "importance_score": 0.28,
        "description": "The customer's monthly bill amount."
      },
      {
        "feature": "total_charges",
        "importance_score": 0.22,
        "description": "Total amount the customer has paid."
      },
      {
        "feature": "internet_service",
        "importance_score": 0.10,
        "description": "Type of internet service the customer has (e.g., DSL, Fiber optic)."
      },
      {
        "feature": "online_security",
        "importance_score": 0.05,
        "description": "Whether the customer has online security."
      }
    ],
    "summary": "The model's predictions are most influenced by contract length, monthly charges, and total charges.  Internet service and online security have a smaller, but still significant, impact."
  },
  "local_explanation": {
    "_comment": "Explanation for a specific instance/prediction.",
    "instance_id": "customer_123",
    "predicted_class": "Churn",
    "prediction_probability": 0.85,
    "feature_contributions": [
      {
        "feature": "contract_length",
        "contribution": -0.40,
        "value": "Month-to-month",
        "reason": "Month-to-month contracts are highly correlated with churn."
      },
      {
        "feature": "monthly_charges",
        "contribution": 0.25,
        "value": 75.50,
        "reason": "Higher monthly charges increase the likelihood of churn."
      },
      {
        "feature": "total_charges",
        "contribution": -0.10,
        "value": 200.00,
        "reason": "Relatively low total charges suggest the customer is new and more likely to churn."
      },
      {
        "feature": "internet_service",
        "contribution": 0.05,
        "value": "Fiber optic",
        "reason": "Fiber optic service is associated with higher churn rates in this dataset."
      },
      {
        "feature": "online_security",
        "contribution": -0.02,
        "value": "No",
        "reason": "Lack of online security slightly increases churn risk."
      }
    ],
    "summary": "This customer is predicted to churn primarily due to their month-to-month contract and high monthly charges. The relatively low total charges also contribute to the prediction."
  },
  "fairness_metrics": {
    "_comment": "Metrics for assessing fairness across different groups.",
    "protected_attribute": "gender",
    "metric": "Disparate Impact",
    "value": 0.95,
    "threshold": 0.8,
    "status": "Acceptable",
    "summary": "The model exhibits acceptable disparate impact across genders, as the value (0.95) is above the threshold (0.8)."
  },
  "data_bias_detection": {
    "_comment": "Results of data bias detection.",
    "potential_bias": "Unequal representation of geographic regions in the training data.",
    "recommendations": [
      "Collect more data from underrepresented regions.",
      "Use re-weighting techniques to balance the data."
    ]
  }
}

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Model Explanation</title>
    <style>
        /* Basic Reset */
        body, h1, h2, h3, p, ul, li {
            margin: 0;
            padding: 0;
            border: 0;
        }

        /* General Styling */
        body {
            font-family: sans-serif;
            line-height: 1.6;
            background-color: #f4f4f4;
            color: #333;
            padding: 20px;
        }

        .container {
            max-width: 960px;
            margin: 0 auto;
            background-color: #fff;
            padding: 20px;
            border-radius: 8px;
            box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1);
        }

        h1 {
            font-size: 2.5rem;
            margin-bottom: 20px;
            text-align: center;
            color: #2c3e50;
        }

        h2 {
            font-size: 1.8rem;
            margin-bottom: 15px;
            color: #34495e;
        }

        h3 {
            font-size: 1.4rem;
            margin-bottom: 10px;
            color: #5b6c7f;
        }

        p {
            margin-bottom: 15px;
        }

        ul {
            list-style: disc;
            margin-left: 20px;
            margin-bottom: 15px;
        }

        /* Explanation Specific Styling */
        .explanation-section {
            margin-bottom: 30px;
            border-bottom: 1px solid #ddd;
            padding-bottom: 20px;
        }

        .explanation-section:last-child {
            border-bottom: none;
        }

        .feature-importance {
            margin-top: 10px;
        }

        .feature-importance table {
            width: 100%;
            border-collapse: collapse;
        }

        .feature-importance th, .feature-importance td {
            border: 1px solid #ddd;
            padding: 8px;
            text-align: left;
        }

        .feature-importance th {
            background-color: #f2f2f2;
        }

        /* Responsive Design */
        @media (max-width: 768px) {
            .container {
                padding: 15px;
            }

            h1 {
                font-size: 2rem;
            }

            h2 {
                font-size: 1.5rem;
            }

            h3 {
                font-size: 1.2rem;
            }

            .feature-importance table {
                font-size: 0.9rem;
            }
        }
    </style>
</head>
<body>
    <div class="container">
        <h1>Model Explanation</h1>

        <div class="explanation-section">
            <h2>Model Summary</h2>
            <p><strong>Model Type:</strong> {{model_type}}</p>
            <p><strong>Model Description:</strong> {{model_description}}</p>
        </div>

        <div class="explanation-section">
            <h2>Feature Importance</h2>
            <div class="feature-importance">
                <p>The table below shows the importance of each feature in the model:</p>
                <table>
                    <thead>
                        <tr>
                            <th>Feature</th>
                            <th>Importance</th>
                        </tr>
                    </thead>
                    <tbody>
                        {{feature_importance_table}}
                    </tbody>
                </table>
            </div>
        </div>

        <div class="explanation-section">
            <h2>Example Prediction Explanation</h2>
            <h3>Input Data:</h3>
            <p>{{example_input_data}}</p>
            <h3>Prediction:</h3>
            <p>{{example_prediction}}</p>
            <h3>Explanation:</h3>
            <p>{{example_explanation}}</p>
        </div>

        <div class="explanation-section">
            <h2>Overall Insights</h2>
            <p>{{overall_insights}}</p>
        </div>
    </div>
</body>
</html>

/*
  visualization_styles.css - CSS file for styling visualizations of feature importance and model predictions.

  This file provides the styling for visualizations generated by the model-explainability-tool plugin.
  It includes styles for feature importance charts, prediction breakdowns, and other interpretability
  visualizations.

  Feel free to customize these styles to match your application's design.

  Table of Contents:
  1. General Styles
  2. Feature Importance Chart Styles
  3. Prediction Breakdown Styles
  4. [Add more sections as needed]
*/

/* 1. General Styles */

body {
  font-family: sans-serif;
  margin: 0;
  padding: 0;
}

.visualization-container {
  margin: 20px;
  padding: 20px;
  border: 1px solid #ccc;
  border-radius: 5px;
}

.visualization-title {
  font-size: 1.5em;
  font-weight: bold;
  margin-bottom: 10px;
}

.visualization-description {
  font-size: 1em;
  color: #555;
  margin-bottom: 20px;
}

/* 2. Feature Importance Chart Styles */

.feature-importance-chart {
  /* Styles for the chart container */
  width: 100%;
  height: 400px; /* Adjust as needed */
}

.feature-importance-bar {
  /* Styles for individual bars in the chart */
  background-color: #4CAF50; /* Green */
  color: white;
  padding: 5px;
  margin-bottom: 2px;
  text-align: right;
}

.feature-name {
  /* Styles for feature names displayed in the chart */
  float: left;
}

.feature-importance-value {
  /* Styles for feature importance values */
  float: right;
}

/* 3. Prediction Breakdown Styles */

.prediction-breakdown {
  /* Styles for the overall prediction breakdown container */
}

.prediction-breakdown-item {
  /* Styles for individual items in the breakdown */
  margin-bottom: 5px;
}

.prediction-breakdown-label {
  /* Styles for labels in the breakdown */
  font-weight: bold;
}

.prediction-breakdown-value {
  /* Styles for values in the breakdown */
  color: #007bff; /* Blue */
}

/* 4.  [Add more sections as needed. Example: Styles for specific model types] */
/* Example: Styles for tree-based models */

.tree-node {
    /* Styles for nodes in a decision tree visualization */
    border: 1px solid #ddd;
    padding: 5px;
    margin: 2px;
    border-radius: 3px;
}

/* Placeholders for future customization */

/* Example: Add styles for different color palettes */
/* .color-palette-1 { ... } */

/* Add responsive styles for different screen sizes */
@media (max-width: 768px) {
  .visualization-container {
    margin: 10px;
    padding: 10px;
  }

  .feature-importance-chart {
    height: 300px; /* Adjust height for smaller screens */
  }
}

Related skills

Microsoft FoundryDeploy, evaluate, and continuously improve Microsoft Foundry agents from a single agent interface.490k1.3k

Ai Research ReproductionOrchestrate trustworthy, auditable reproduction of deep learning repositories directly from their READMEs.176k511

Run TrainSafely execute selected deep learning training commands with standardized evidence capture.176k511

Explore RunSafely run isolated exploratory experiments with clear recording and conservative selection before committing changes.176k511

Paper Context ResolverFetch precise reproduction-critical details like dataset splits, preprocessing steps, or evaluation protocols from the original academic paper when the repo README leav141k511

Repo Intake And PlanScan unfamiliar AI research repositories and receive a minimal, trustworthy reproduction target before investing significant time.140k511

Data Science & MLanalytics

Explaining Machine Learning Models

About

Explaining Machine Learning Models by the numbers

Add your badge

What it does

Files

Model Explainability Tool

Overview

How It Works

When to Use This Skill

Examples

Example 1: Understanding Loan Application Decisions

Example 2: Identifying Key Factors in Customer Churn

Best Practices

Integration

Prerequisites

Instructions

Output

Error Handling

Resources

Assets

References

Scripts

Auto-Generated

Related skills

About

Explaining Machine Learning Models by the numbers

Add your badge

What it does

Files

Model Explainability Tool

Overview

How It Works

When to Use This Skill

Examples

Example 1: Understanding Loan Application Decisions

Example 2: Identifying Key Factors in Customer Churn

Best Practices

Integration

Prerequisites

Instructions

Output

Error Handling

Resources

Assets

References

Scripts

Auto-Generated

Related skills

This week in AI coding