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学生对 Coursera Project Network 提供的 Explainable Machine Learning with LIME and H2O in R 的评价和反馈

54 个评分


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....



Aug 5, 2020

A Nice choice of the contents in this course, I must say! A good guided that I should recommend everyone to take. Good luck!


Jul 15, 2020

It was an interesting course, explaining hat is happening inside a machine learning algorithm.


1 - Explainable Machine Learning with LIME and H2O in R 的 9 个评论(共 9 个)

创建者 Khandaker M A

Aug 6, 2020

创建者 Lasai B T

Nov 24, 2020

创建者 Maria S

Jul 16, 2020

创建者 Cheikh B

Nov 19, 2020

创建者 H. D S

Aug 10, 2021

创建者 Chow K M

Mar 6, 2022

创建者 Kadek A W

Jul 8, 2020


Jul 7, 2020

创建者 Simon S R

Sep 2, 2020