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Back to Explainable Machine Learning with LIME and H2O in R

Learner Reviews & Feedback for Explainable Machine Learning with LIME and H2O in R by Coursera Project Network

4.7
stars
55 ratings

About the Course

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....

Top reviews

KA

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!

MS

Jul 15, 2020

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

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1 - 10 of 10 Reviews for Explainable Machine Learning with LIME and H2O in R

By Khandaker M A

•

Aug 6, 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!

By Lasai B T

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Nov 24, 2020

This course is great. The instructor and the tools to follow the explanations are awesome. Thank you!

By Maria S

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Jul 16, 2020

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

By Cheikh B

•

Nov 19, 2020

The best project and one the best instructors in Coursera projects

By Adam H

•

Feb 15, 2023

Great intro into these packages. Easy to follow and understand.

By H. D S

•

Aug 10, 2021

Great intro to machine learning and model intrepretation

By Chow K M

•

Mar 6, 2022

Found the exposure to h2o and lime helpful. Thank you.

By Kadek A W

•

Jul 8, 2020

Very good course, everything was just right

By ARAVIND K R

•

Jul 7, 2020

It's very informative and usefull to me

By Simon S R

•

Sep 2, 2020

Theory of Lime should be more highlighted!

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