sagemaker-explaining-credit-decisions VS interpret

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.

Maybe InterpretML? It's developed and maintained by Microsoft Research and consolidates a lot of different explainability methods.

You may also find Explainable Boosting Machines interesting: https://github.com/interpretml/interpret

They're a bit like a best of both worlds between linear models and random forests (generalized additive models fit with boosted decision trees)

Disclosure: I helped build this open source package

Microsoft Explainable Boosting Machine (which is a Gaussian Additive Model and not a Gradient Boosted Trees 🙄 model) is a step in that direction https://github.com/interpretml/interpret

Also I'd recommend everyone who works with xgboost to give EBM's a try! They perform comparably (except in the case of extreme interactions) but are actually interpretable! https://github.com/interpretml/interpret/ Beside that they since on runtime they're practically a lookup table they're very quick (at the cost of longer training time).

Open source code by Microsoft: https://github.com/interpretml/interpret (called EBM in this implementation).

If it's not an image, have a go at Microsoft's Explainable Boosting Maching) https://github.com/interpretml/interpret which is not a GBM but a GAM (Gradient Boosting Machine vs Gradient Additive Model). This will also give you explanation via SHAP or LIME values.