I’ll begin by discussing counterfactuals example. Suppose we have a machine learning model that predicts the likelihood of a patient developing diabetes, with one of its features is the patient’s glucose level. In this scenario, the model predicts that a patient has a high likelihood of developing diabetes, based on their glucose level. Now we may wonder what glucose level would be required for the prediction to change to not indicate diabetes. To answer this question, we can run a counterfactual analysis using the machine learning model, where we simulate changing the patient’s glucose level and observe how this affects the prediction. So, with this example, we can see that counterfactual can be used for interpreting (its not casual relationship) model prediction for specific instance.

Decorator is one of design pattern type. It allow you to add additional functionality to your function or class. For note:this functionality should be out of scope your function, because your function should have one thing to do. And to achieve this, we just create function that accept main function as argument

If we want to share our jupyter notebook, but want to hide all messy code behind it, we can use the following command:

To list all docker container that been running on our machine, we can use the following command:

As a developer, writing documentation is tedious task because it’s easy to get write block. Here’s some point that maybe can help to make good documentation.