Explainable AI in Python
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Learn to explain machine learning predictions in Python with feature importance, SHAP and LIME, and to test how reliable those explanations are.
Explainable AI is about showing why a model reached a decision. You work in Python to trace those decisions, reading rules out of decision trees and feature impact out of regression models, then applying SHAP and LIME to see which features drive a model overall and a single prediction. Later lessons check whether the explanations hold up and extend the methods to clustering and chat-based generative models.
What You Will Achieve
- Extract decision rules from a tree and compare what linear and tree-based models treat as their strongest features
- Rank the inputs behind an MLP classifier or regressor using permutation importance and SHAP values
- Explain one prediction on tabular, text or image data and show the factors that pushed it
- Test explanations for consistency and faithfulness, and probe a chat model’s reasoning with chain-of-thought prompts
Program Overview
Model-Specific Explainability in Linear and Tree Models
Contrasts model-specific and model-agnostic approaches, then derives feature impact from regression and feature importance from tree ensembles.
- Decision trees versus neural networks
- Feature impact with linear and logistic regression
- Feature importance with decision trees and random forests
Model-Agnostic and Local Explainability with SHAP and LIME
Measures feature influence across any model, then narrows the view to individual predictions.
- Permutation importance and SHAP kernel explainers
- Feature importance, beeswarm and partial dependence plots
- Global versus local explanations
- LIME for regressors, classifiers, sentiment text and food images
Explainability Metrics, Unsupervised and Generative Models
Judges how reliable explanations are and applies explainability to clustering and chat-based generative AI.
- SHAP consistency and LIME faithfulness
- Feature impact on cluster quality with ARI
- Chain-of-thought and self-consistency
Certificate Awarded (Paid)
Job Outlook
Explaining model behavior is useful wherever machine learning supports decisions people need to trust, such as when debugging a model or meeting regulatory requirements.
Good for: Practitioners who already build machine learning models in Python and need to show which features drive their predictions.
Keep in mind: The generative AI material is a few closing lessons on chain-of-thought and self-consistency; most of the work explains tabular models with scikit-learn, SHAP and LIME.
Bottom line: A hands-on run through SHAP and LIME that also asks whether the explanations themselves deserve trust.
Frequently Asked Questions
Which Python libraries do the exercises rely on?
Models are built and inspected with scikit-learn, and the lessons explain MLPClassifier and MLPRegressor neural networks as well as linear and tree models. SHAP supplies feature attributions and kernel explainers for global and local views, and LIME explains individual predictions on tabular, text and image inputs.
What kinds of data get explained in the lessons?
Most exercises use tabular data, with cases on medical charges, heart disease, admissions and income levels. LIME is also applied to a sentiment analysis model and to food image predictions, and a clustering example looks at which features shape cluster quality.
How are chat-based generative models handled?
The final chapter turns to chat-based models such as ChatGPT. It uses chain-of-thought prompting to bring out the steps behind an answer and self-consistency to judge how confident the model is, rather than the feature attribution methods used earlier.
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