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學生對 Coursera Project Network 提供的 Explainable Machine Learning with LIME and H2O in R 的評價和反饋
52 個評分
課程概述
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.
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KA
2020年8月5日
A Nice choice of the contents in this course, I must say! A good guided that I should recommend everyone to take. Good luck!
MS
2020年7月15日
It was an interesting course, explaining hat is happening inside a machine learning algorithm.
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1 - Explainable Machine Learning with LIME and H2O in R 的 9 個評論（共 9 個）
創建者 Khandaker M A
•2020年8月6日
創建者 Lasai B T
•2020年11月24日
創建者 Maria S
•2020年7月16日
創建者 Cheikh B
•2020年11月19日
創建者 H. D S
•2021年8月10日
創建者 Chow K M
•2022年3月6日
創建者 Kadek A W
•2020年7月8日
創建者 ARAVIND K R
•2020年7月7日
創建者 Simon S R
•2020年9月2日
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