Let's now explore the capabilities of the WIT tool with an example. The example has been taken from the demos provided on the website and is called Income Classification wherein we need to predict whether a person earns more than $50k a year, based on their census information. The Dataset belongs to the UCI Census dataset consisting of a number of attributes such as age, marital status and education level. Let's begin by doing some Exploration of the dataset. Here is a link to the web Demo for following along.

In this era of explainable and interpretable Machine Learning, one merely cannot be content with simply training the model and obtaining predictions from it. To be able to really make an impact and obtain good results, we should also be able to probe and investigate our models. Apart from that, algorithmic fairness constraints and bias should also be clearly kept in mind before going ahead with the model. Investigating a model requires asking a lot of questions and one needs to have an acumen of a detective to probe and look for issues and inconsistencies within the models. Also, such a task is usually complex requiring to write a lot of custom code.