Explainable and Interpretable Artificial Intelligence : 1

Explore essential math symbols used in explainable and interpretable artificial intelligence, including limits, epsilon, derivatives, partial derivatives, integrals, gradients, jacobian, hessian, laplacian, transforms, and probability.

Explore core statistical and set theory symbols used in data analysis and optimization, including mu, sigma, rho, and covariance. Include variance, supremum, infimum, and set operations like union and intersection.

Explore explainable AI and interpretable AI, compare post-hoc tools like Shap values, Lime, attention maps with transparent models, and understand the accuracy-interpretability trade-off in high-stakes domains.

Explore shapley additive explanations (shap) that break a model’s prediction into feature contributions using efficiency, symmetry, dummy, and additivity, enabling local and global interpretations for healthcare, finance, and marketing.

Explainable and interpretable ai uses lime to explain a model's predictions by building a simple local model around a single instance with perturbed samples and local weights.

Learn to interpret a random forest on california housing using Shapley additive explanations (SHAP) values, with summary, force, and feature-importance plots to explain individual predictions.

Explain how lime provides local explanations for text sentiment predictions by highlighting influential words in a logistic regression classifier, using a small tf-idf pipeline and visual bars.

Explore a step-by-step Python implementation of lime for a random forest, generating perturbed samples, weighting by a gaussian kernel, training a local linear model, and revealing feature importance.

Build a text classification pipeline using tfidfvectorizer and logistic regression on the 20 newsgroups dataset to separate atheism and Christianity, and generate LIME explanations for predictions.

Train a random forest on the breast cancer dataset to predict malignant versus benign tumors. Use lime to generate and interpret local explanations, showing top feature contributions visually and textually.

Train a random forest regressor on the California housing dataset and plot the partial dependence of median income on predicted housing prices, revealing a positive but non-linear relationship.

Explore eli5, the post-hoc python library that makes machine learning models interpretable through local explanations, global insights, and permutation importance across linear, tree-based, ensemble, and neural models.

Explore how the scatter library interprets machine learning models, providing global and local explanations, feature importances, PDPs, and lime explanations for both local and deployed models.

Explore how Captum analyzes a simple PyTorch CNN with synthetic RGB data, applying integrated gradients, saliency maps, deep lift, and guided Grad-Cam to reveal the model's influential input regions.

Explore how attribution maps and heatmaps from integrated gradients, saliency, smoothgrad, deep lift, and guided grad-cam reveal the model's focus on colored squares in a synthetic image.

Shap stands for Shapley additive explanations and allocates each feature's contribution to a prediction. Rooted in cooperative game theory, it uses Shapley values to ensure local accuracy and additivity.

Explore explainable boosting machines (ABMs), a glass box model that combines gradient boosting and bagging with generalized additive models for interpretable predictions. Gain global and local interpretation.

Explore partial dependence plots (pdps) as tools to interpret machine learning models, showing how single or two-dimensional feature interactions affect predictions, with bike rentals and cervical cancer risk examples.

Identify all relevant features for explainable ai by using Boruta, a random-forest based feature selection method that uses shadow features and permutation importance to boost interpretability and trust.

Explore how LIME provides local, interpretable explanations for any model by building a simple, model-agnostic predictor around a single instance in its neighborhood.

Conclude with a solid starting toolkit for explainable and interpretable artificial intelligence, using shap, lime, PDP, Eli5, scatter, and Captum in Python to make model predictions transparent.