A Guide to Automated Testing of Machine Learning Models — Chapter 1

Machine learning models are becoming increasingly prevalent in many applications, from image and speech recognition to fraud detection and medical diagnosis. As these models become more complex and are applied to more critical tasks, it is important to ensure their correctness and robustness. One way to achieve this is through automated testing.

Automated testing of machine learning models involves testing the model’s behavior under a wide range of scenarios and inputs, to ensure that it is working as intended and that its output is correct and consistent.

This mini-series will be divided into multiple parts, the idea is to keep it short and simple so that person with no experience or knowledge around machine learning can pick it up very easily.

In this chapter we will be focusing on testing ‘DecisionTreeClassifier’ which is a classification model based on the decision tree algorithm. It works by recursively splitting the data into subsets based on the values of a feature, until each subset is pure (contains only one class label) or a maximum depth is reached. It makes decisions by traversing the tree from the root to a leaf node based on the values of the features of the input data, and the class label associated with the leaf node is the predicted class label of the input data…