When I started working with different Data science models , I often asked myself about the quality of output in real world (irrespective of accuracy metrics). Most of the times , as Data scientist you get Test data and you have no idea of the BIAS that is build inside the data but you produce a model that may have high accuracy metrics . Machine learning models are used in various industries where bias in the data can lead to very high impacting decisions .
When you are using simple models (Linear or Logistic regression) , one is able to explain results for sample data set . Normally these models does not suffice and we end up using Deep learning models which provided high performance but are black box to most of Data Science practitioners. Machine learning models are now used to make lot of critical decisions — Fraud detections , Credit rating , Self driving , Examining patients etc .
There are five major frameworks which can give us deep insights into the model predictions
1.ELI5
2.LIME
3.SHAP
4.Microsoft Interpret ML
5.Skater
In this Repo , I will provide Notebooks explaining each of them . Detailed articles are also avaible by me on Medium.