Explainable Machine Learning with LIME and H2O in R

4.7
별점
50개의 평가
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학습자는 이 안내 프로젝트에서 다음을 수행하게 됩니다.

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
Intermediate중급
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

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.

개발할 기술

  • r-programming-language
  • data-science
  • LIME
  • machine-learning
  • H2O

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  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

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

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EXPLAINABLE MACHINE LEARNING WITH LIME AND H2O IN R의 최상위 리뷰

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