Lion Cipher Lab
Explain a single model prediction with LIME
- AI Security
- Beginner
- ~40 min
- Free · no account required
Why it matters
Models are increasingly 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.' LIME is one of the two best-known open-source ways to answer that for a single prediction, and it is worth learning precisely because it works differently from SHAP -- it treats the model as a sealed box and probes it from the outside. Learning…
What you'll learn
- explainable AI (XAI)
- LIME basics
- local perturbation-based explanations
- reading a single prediction's feature contributions
- model-agnostic explanation
- reasoning about the limits and instability of explanations
Steps in this lab
- Why a model needs to explain itself
- How LIME works, in plain terms
- Install LIME and scikit-learn
- Train a tiny model of your own
- Point LIME at the model
- Explain one prediction and read it
- The limits: local, unstable, approximate, and gameable
- What you carry out of this lab