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Learn AI with Python · Lesson

LIME: Local Interpretable Explanations

lime.tabular.LimeTabularExplainer, perturbing input, local linear approximation.

LIME: Local Interpretable Explanations is a free Learn AI with Python lesson on CoddyKit — lesson 3 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 Learn AI with Python learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

What is LIME

LIME (Local Interpretable Model-agnostic Explanations) explains a single prediction by fitting a simple, interpretable model (like linear regression) to the complex model behavior in a small neighborhood around that one instance.

Local, Not Global

A black-box model may be wildly nonlinear overall, but near one point it is approximately linear. LIME exploits this: it does not explain the whole model, only its behavior locally around the instance you care about.

How LIME Works

LIME perturbs the instance to create many nearby samples, asks the black-box model to label them, weights samples by closeness to the original, then fits an interpretable surrogate. The surrogate coefficients become the explanation.

Model-Agnostic

Because LIME only calls the model prediction function, it works with any classifier or regressor: neural networks, gradient boosting, even an external API. It treats the model as a black box.

LimeTabularExplainer

For tabular data you build a LimeTabularExplainer from the training data and feature names. It uses the training distribution to generate realistic perturbations.

from lime.lime_tabular import LimeTabularExplainer

explainer = LimeTabularExplainer(
    training_data=X_train.values,
    feature_names=X_train.columns.tolist(),
    class_names=["denied", "approved"],
    mode="classification",
)

explain_instance

To explain one row, call explain_instance with the row and the model predict_proba function. LIME perturbs around that row and fits the local surrogate.

exp = explainer.explain_instance(
    data_row=X_test.values[0],
    predict_fn=model.predict_proba,
    num_features=5,
)

predict_proba, not predict

LIME for classification needs probabilities, not hard labels, because the surrogate regresses on the probability surface. Always pass model.predict_proba (or any function returning class probabilities).

The num_features Parameter

num_features caps how many features appear in the explanation. A smaller number yields a simpler, more digestible explanation that highlights only the strongest local drivers.

Reading Feature Weights

exp.as_list() returns the explanation as (feature condition, weight) pairs. A positive weight pushed the prediction toward the predicted class; a negative weight pushed away.

for feature, weight in exp.as_list():
    print(feature, round(weight, 3))
# income > 50000     0.21
# debt_ratio > 0.4  -0.18

Visualizing the Explanation

LIME can render the explanation as an interactive chart or notebook view, showing the contribution bars and the predicted probabilities side by side.

exp.show_in_notebook(show_table=True)
# or
fig = exp.as_pyplot_figure()

LIME vs SHAP

Both explain individual predictions, but differ:

  • SHAP has strong theoretical guarantees (additivity, consistency) and a global view
  • LIME is faster to grasp, model-agnostic, and intuitive, but its local surrogate is an approximation that can vary with perturbation randomness

Quick Check

Test your LIME knowledge.

Recap

You learned local explanations with LIME:

  • LimeTabularExplainer is built from training data and feature names
  • explain_instance uses model.predict_proba to fit a local surrogate
  • num_features controls explanation size
  • as_list() returns signed feature weights
  • LIME is model-agnostic and local, complementing SHAP

Frequently asked questions

Is the “LIME: Local Interpretable Explanations” lesson free?

Yes — the full text of “LIME: Local Interpretable Explanations” is free to read here on the web, and the Learn AI with Python 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 Learn AI with Python course, upgrade to CoddyKit PRO.

What will I learn in “LIME: Local Interpretable Explanations”?

lime.tabular.LimeTabularExplainer, perturbing input, local linear approximation. You practise Learn AI with Python 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 Learn AI with Python?

No prior experience is required. Learn AI with Python on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “LIME: Local Interpretable Explanations” 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 Learn AI with Python lesson?

Yes. Every Learn AI with Python 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

  1. Bias Detection in ML Models
  2. SHAP Values for Model Explainability
  3. LIME: Local Interpretable Explanations
  4. AI Ethics and Governance Frameworks
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