Marc Hansen7. SHAP — Scikit, No Tears 0.0.1 documentationoneoffcoder.com7. SHAP SHAP’s goal is to explain machine learning output using a game theoretic approach. A primary use of SHAP is to understand how variables and …
Marc HansenFlipboardIcon version of the Flipboard logoLearn Machine Learning Explainability Tutorialskaggle.comExtract human-understandable insights from any model.
Marc HansenExplaining Machine Learning Models: A Non-Technical Guide to Interpreting SHAP Analysesaidancooper.co.uk - Aidan CooperWith interpretability becoming an increasingly important requirement for machine learning projects, there's a growing need to communicate the complex …
Marc HansenShapley Value For Interpretable Machine Learninganalyticsvidhya.com• Learn how to use Shapley values in game theory for machine learning interpretability • It’s a unique and different perspective to interpret black-box …
Marc HansenGitHub - microsoft/EconML: ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.github.com - microsoftEconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation News Getting Started Installation Usage Examples Estimation Methods …
