Explainable Machine Learning with LIME and H2O in R

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Coursera Project Network
在此指導項目中,您將:

Use LIME and H2O for automatic and interpretable machine learning

Build Classification Models with AutoML

Explain and Interpret the Model Predictions using LIME

Clock2 hours
Intermediate中級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

Welcome to this hands-on, guided introduction to Explainable Machine Learning with LIME and H2O in R. By the end of this project, you will be able to use the LIME and H2O packages in R for automatic and interpretable machine learning, build classification models quickly with H2O AutoML and explain and interpret model predictions using LIME. Machine learning (ML) models such as Random Forests, Gradient Boosted Machines, Neural Networks, Stacked Ensembles, etc., are often considered black boxes. However, they are more accurate for predicting non-linear phenomena due to their flexibility. Experts agree that higher accuracy often comes at the price of interpretability, which is critical to business adoption, trust, regulatory oversight (e.g., GDPR, Right to Explanation, etc.). As more industries from healthcare to banking are adopting ML models, their predictions are being used to justify the cost of healthcare and for loan approvals or denials. For regulated industries that use machine learning, interpretability is a requirement. As Finale Doshi-Velez and Been Kim put it, interpretability is "The ability to explain or to present in understandable terms to a human.". To successfully complete the project, we recommend that you have prior experience with programming in R, basic machine learning theory, and have trained ML models in R. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

您要培養的技能

r-programming-languagedata-scienceLIMEmachine-learningH2O

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  1. Introduction and Project Overview

  2. Import Libraries and Load the IBM HR Employee Attrition Data

  3. Preprocess Data using Recipes

  4. Start H2O Cluster and Create Train/Test Splits

  5. Run AutoML to Train and Tune Models

  6. Leaderboard Exploration

  7. Model Performance Evaluation

  8. Local Interpretable Model-Agnostic Explanations (LIME)

  9. Apply LIME to Interpret Model Outcomes

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