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model-explanation

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Turn XGBoost decisions into SHAP insights.

Data & Analytics#model interpretability#feature importance#shap#xgboost#interaction analysis#single-sample explanation
Authoraliyun
Version1.0.0
Installs0

System Documentation

What problem does it solve?

Help you explain why an XGBoost model predicts a certain outcome by attributing prediction contributions to features, so stakeholders can interpret both overall behavior and individual decisions.

Core Features & Use Cases

  • Global feature importance (SHAP): Identify the most influential features driving model predictions across the dataset, useful for reporting and model debugging.
  • Single-sample prediction explanation: Explain a specific sample’s predicted probability by highlighting top contributing features and their direction (positive/negative).
  • Feature interaction analysis: Analyze how two features interact to affect predictions, supporting deeper reasoning about model behavior in complex financial signals.

Quick Start

Use model-explanation with a trained XGBoost model file and a dataset by running: python scripts/explainer.py --model_path ./models/my_model.json --data_path ./examples/toy.parquet --target y_label --output_dir ./outputs/explain_run

Dependency Matrix

Required Modules

shapmatplotlibxgboostpandasnumpy

Components

scripts

💻 Claude Code Installation

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Please help me install this Skill:
Name: model-explanation
Download link: https://github.com/aliyun/qwen-dianjin/archive/main.zip#model-explanation

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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