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Вернуться к Explainable Machine Learning with LIME and H2O in R

Отзывы учащихся о курсе Explainable Machine Learning with LIME and H2O in R от партнера Coursera Project Network

4.7
звезд
Оценки: 50
Рецензии: 8

О курсе

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....

Лучшие рецензии

KA
5 авг. 2020 г.

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
15 июля 2020 г.

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

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1–8 из 8 отзывов о курсе Explainable Machine Learning with LIME and H2O in R

автор: Khandaker M A

6 авг. 2020 г.

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

автор: Lasai B T

24 нояб. 2020 г.

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

автор: Maria S

16 июля 2020 г.

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

автор: Cheikh B

19 нояб. 2020 г.

The best project and one the best instructors in Coursera projects

автор: H. D S

10 авг. 2021 г.

G​reat intro to machine learning and model intrepretation

автор: Kadek A W

8 июля 2020 г.

Very good course, everything was just right

автор: ARAVIND K R

7 июля 2020 г.

It's very informative and usefull to me

автор: Simon S R

2 сент. 2020 г.

Theory of Lime should be more highlighted!