byuibigdata / flc_model_explainers-1

This is for the FLC team teaching about Model Explainers

FLC_Model_Explainers

What is a model explainer? - James

What is a model explainer? A model explainer is a function designed to show how the model produces different predictions. Models in 2D are relatively easy to describe. For example, there we could have a classification model determined by a single line.

However, models frequently have more than 2 features, and so cannot be easily placed on a single chart. However, a model explainer can help us answer the following questions. Which features are the most important? How does changing those features impact the prediction? Is the model more complicated than it needs to be?

What does the information mean that the model explainer gives us? - Richard

This information allows us to see inside the Blackbox of what a ML model is doing.

Various Model Explainers - Derek

https://www.analyticsvidhya.com/blog/2020/03/6-python-libraries-interpret-machine-learning-models/ Depending on the project that you are working on, different model explainers will be able to tell you your results in different ways. There are 6 main model explainers, each with their own personality.

Model Explainer Example Derek LIME

Model Explainer Example Tanner

Model explainer Example Ammon Eli 5

EXPLAIN LIKE IM 5 (ELI 5)

Amazing dashboarding for model explainers

https://titanicexplainer.herokuapp.com/classifier/

(If you have another question that you want to research add it here or below)

resources: https://www.kaggle.com/code/dansbecker/advanced-uses-of-shap-values/tutorial https://medium.com/analytics-vidhya/explain-ml-models-shap-library-5ce375c85d7d https://www.kaggle.com/code/scratchpad/notebook616777f210/edit https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.mllib.tree.GradientBoostedTrees.html https://interpret.ml/docs/lime.html https://github.com/TeamHG-Memex/eli5