Explainable Al (XAI) with Python

Demonstrate glass box models for explainable ai in Python by loading, preprocessing, and exploring the stroke dataset. Implement logistic regression and interpretable models, and address imbalanced data and encoding techniques.

Demonstrates preprocessing for a health care dataset in explainable AI with Python, including one-hot encoding of categorical features, handling missing values, dropping id, and 80/20 train-test split for stroke prediction.

Explore the confusion matrix to evaluate classifier performance, defining true positives, true negatives, false positives, and false negatives, then compute precision, recall, and the F1 score.

Explore the interpretML package to explain glass box and black box models, delivering global and local explanations on the stroke dataset using logistic regression and other classifiers.

Learn how lime provides local, interpretable, model-agnostic explanations for individual predictions of any black-box model using a simple surrogate.

LIME builds a local, sparse linear model around a data point by perturbing features, weighting nearby points, and mimicking a complex model’s predictions for explanation.

Explore explainable ai with python by showing how Shapley values allocate the contribution of age, gender, and job in a black-box income predictor through all feature coalitions and marginal contributions.

Explore how SHAP values are modeled through marginal contributions, binomial coefficients, and reciprocal edge weights, and derive feature attributions for a full model versus a null model.

Explore mathematical modeling of counterfactual explanations in explainable ai with Python, minimally altering input features to flip predictions, comparing brute-force and model-aware approaches, and emphasizing visible counterfactuals and cost metrics.

Demonstrate the what-if tool by visualizing and generating counterfactual explanations, exploring model behavior with preloaded census models, data point edits, and partial dependence plots.

Demonstrates Google's what-if tool to analyze fairness in recidivism classification, exploring data points, counterfactuals, and partial dependence plots to reveal model bias and perform fairness and performance analysis.

Explore explainable ai with python through interactive lrp demos, generating real-time heatmaps that justify handwriting, image, and text classifications and visual question answering decisions.