Skill UI

Provides interpretable explanations for ML predictions using SHAP, LIME, and other techniques, helping debug model behavior, highlight feature importance, and communicate fairness insights to stakeholders.

Automates guidance on assessing feature importance for machine learning training, covering data prep, model training, tuning, and experiment tracking while generating production-ready code, configurations, and validation instructions for best-practice compliance.

Guides using SHAP to explain ML predictions, compute feature importance, generate diagnostic plots, debug models, analyze bias, and compare models across tree, deep, linear, or black-box architectures.

Structured pipeline for credit risk pre-loan modeling that loads and formats raw data, filters abnormal periods, trims high-missing/low-IV/high-PSI/high-correlation features, denoises via Null Importance, and exports an Excel cleaning report for review.

SHAP (SHapley Additive exPlanations) provides a unified, robust framework for understanding complex machine learning models. It calculates feature importance using Shapley values from game theory, allowing users to interpret individual predictions, analyze model bias, visualize feature interactions, and debug model behavior. Applicable across deep learning, tree-based, and black-box models.