SHAP

Lesson in the MLOps Roadmap · Stage 12: Edge AI

Last updated 14 September 2026

What is SHAP?

SHAP (SHapley Additive exPlanations) is a method used to explain the output of any machine learning model. It uses concepts from game theory to assign each feature a value representing its contribution to the prediction. These values, known as SHAP values, indicate the degree to which each feature contributed to the model's output for a specific instance, facilitating a deeper understanding of the model's decision-making process.

Study this in the interactive course See the whole MLOps roadmap

Free resources to learn SHAP

Official docsWelcome to the SHAP documentation
Open sourceshap
ArticleExplainable AI - Understanding and Trusting Machine Learning Models
ArticleWhen Shapley Values Break: A Guide to Robust Model Explainability
VideoSHAP values for beginners | What they mean and their applications

SHAP: common questions

Can I learn SHAP in Hindi?

This lesson is written in English. Once you sign in, open it in the interactive course and ask the AI tutor to explain SHAP in Hindi or Hinglish. The tutor answers from this lesson’s own text, so it stays on the syllabus, and it runs on your own Google, OpenAI or Anthropic key.

Is this SHAP tutorial for beginners?

It is lesson 61 of 62 in the free MLOps roadmap, in stage 12, Edge AI. If you are new to MLOps, start from stage 1 and come back here in order; if you already know the earlier stages, this lesson stands on its own.