Explain Predictions with SHAP
Attribute each output to its input features.
Explain Predictions with SHAP is a free MLOps Academy lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the MLOps Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Why Explain a Prediction
Stakeholders rarely accept a number with no reason. Explainability shows which inputs drove a single output. 💡
What SHAP Is
SHAP assigns each feature a value showing how much it pushed one prediction up or down from a baseline.
The Game Theory Roots
SHAP comes from Shapley values in game theory, which fairly split a payout among players who cooperate.
Features as Players
SHAP treats each feature as a player and the prediction as the payout, then shares credit for the result fairly among them.
The Base Value
Explanations start from a base value, the average prediction. Each feature then nudges the output toward its final value.
Build an Explainer
You wrap your trained model in a SHAP explainer, then compute values for the rows you want to understand.
import shap
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_sample)Read One Prediction
A local explanation breaks down a single row: maybe high income pushed approval up while a short history pulled it down.
See the Whole Model
Averaging absolute SHAP values across many rows gives global importance, ranking which features matter most overall.
Pick the Right Explainer
Use TreeExplainer for tree models, it is fast and exact. KernelExplainer is slower but works with almost any model.
SHAP for Debugging
When a slice underperforms, SHAP can reveal the model is leaning on a leaky or spurious feature it should ignore.
Mind the Cost
SHAP is compute-heavy, so explain a sample or run it offline rather than on every live request.
Quick Check
What does a SHAP value actually tell you?
Recap
SHAP fairly splits a prediction among its features, giving local and global explanations that complete the observability picture. ✅
Frequently asked questions
Is the “Explain Predictions with SHAP” lesson free?
Yes — the full text of “Explain Predictions with SHAP” is free to read here on the web, and the MLOps Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the MLOps Academy course, upgrade to CoddyKit PRO.
What will I learn in “Explain Predictions with SHAP”?
Attribute each output to its input features. You practise MLOps Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start MLOps Academy?
No prior experience is required. MLOps Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Explain Predictions with SHAP” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this MLOps Academy lesson?
Yes. Every MLOps Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- The Four Pillars of ML Observability
- Log Predictions for Later Analysis
- Slice Metrics by Segment and Cohort
- Explain Predictions with SHAP