Lion Cipher Lab
Explain why a model made a prediction with SHAP
- AI Security
- Beginner
- ~40 min
- Free · no account required
Why it matters
Models are increasingly making or shaping decisions that matter — flagging an alert, scoring a transaction, ranking a resume — and a beginner's most valuable habit is refusing to treat those decisions as unquestionable. Explainability is the concrete skill behind that habit. Being able to ask 'which inputs pushed this particular prediction, and by how much' is what turns a black box into something you can trust on purpose, debug when it is wrong, check for unfair reliance on the wrong features, and defend when someone asks you to justify it. SHAP is the standard open-source way to do this,…
What you'll learn
- explainable AI (XAI)
- SHAP basics
- feature attribution
- reading a single prediction's explanation
- reasoning about the limits of explanations
Steps in this lab
- Why a model needs to explain itself
- What a SHAP value actually is
- Install SHAP and scikit-learn
- Train a tiny model of your own
- Compute SHAP values for the predictions
- Read the explanation for one prediction
- The limits: an explanation is an approximation, not the truth
- What you carry out of this lab