sagemaker-explaining-credit-decisions VS DALEX

Have looked at: https://sagemaker-examples.readthedocs.io/en/latest/advanced_functionality/scikit_bring_your_own/scikit_bring_your_own.html https://docs.aws.amazon.com/sagemaker/latest/dg/docker-containers-create.html https://sagemaker-immersionday.workshop.aws/lab3/option1.html The above don't specify LightGBM, but the concept of Bring Your Own Container/Algorithm is the same. I think this article might be more than what you need, buy it does reference LightGBM https://github.com/awslabs/sagemaker-explaining-credit-decisions And also this one https://github.com/aws-samples/amazon-sagemaker-script-mode/blob/master/lightgbm-byo/lightgbm-byo.ipynb

Yes, SageMaker is an end to end service covering data labeling, data preparation, model training, model deployment, model monitoring, etc. This recent video will give you a grand hands-on tour of SageMaker: https://www.twitch.tv/aws/video/929163653. Training and deployment is based on Docker containers, either built-in (algorithms and open source frameworks) or your own. With respect to LightGBM, you can easily start from the built-in scikit-learn container, and add LightGBM to it. Here's a complete example: https://github.com/awslabs/sagemaker-explaining-credit-decisions.

Which he will not do, because: a) He can't, it's a black box algorithm. It actually is open source already, but that doesn't mean much as it's useless without Twitter's data https://github.com/ModelOriented/DALEX b) He won't release data that shows the algorithm is racist and amplifies conservative and extremist content. He won't remove such functions because it will cost him billions.

There are a couple of ways to use Shapley values for explanations in R. One way is to use DALEX, which also contains a lot of other methods besides SHAP. Another one is iml. I am sure there are several other implementations of SHAP as well.