ML Model Testing: What it is, Benefits & Types of Tests in 2024
AI is perceived as a strategic priority by 83% of businesses, and 54% of executives agree that the implementation of AI improved productivity. However, 78% of the ML projects do not have a chance to reach the deployment phase.
For AI to have a positive impact on productivity, it is important to understand the sources of the challenges faced during model deployment. ML model testing can help businesses achieve this goal, and in this article, we will discuss its benefits and various types.
What is model testing?
Model testing is the process of assessing whether an ML model produces the desired outcome or not. If a model passes the tests, it is ready for deployment. However, if it fails, it must be developed and tested again.
Though evaluation and testing can be used interchangeably, there is a significant difference between them. An evaluation enables the observation of the performance of a model based on the relevant metrics. However, it is not possible to detect the source of the problems through evaluation. Understanding the origin of the problems can be possible via model testing procedures.
What are the benefits of model testing in ML?
ML model testing enables data scientists to conduct quality assurance of data, features, algorithms, or model parameters to:
- Eliminate malfunctions and increase robustness: Conducting different tests to assess different aspects of the model enables the root cause of potential problems.
- Ease the deployment process: It helps to check whether the model works in the intended manner before it is productized.
What are the differences between testing software and ML models?
- The subjects of the tests are the main difference between software testing and ML model testing. Software testing involves testing the code to prevent bugs. On the other hand, ML model testing also involves testing the data and the model as well as the code to ensure that the ML model performs as expected.
- The internal logic of the testing operation also differs in these two contexts. In software testing, the aim is to observe if the software can produce the intended outcome. In ML model testing, what is expected is not the occurrence of a specific behavior, but the smooth functioning of the learned logic of the model. Testing is more complex in ML because unlike fixing a failed unit, it requires interpreting and determining which aspect of the ML model is not functioning.
- There is also a difference in the level of achievement expected from software and ML models. Evaluating a software application and an ML model differs because ML models do not have a deterministic structure. The aim of an ML model is to reach a realistic accuracy rate within a range of 70-90%. The traditional software setting, however, does not allow for a margin of error, since it is not probabilistic.
What are the different types of ML model testing?
Some common testing methods include:
- Manual Error Analysis: This method can be used to determine where the model has errors, if there is a pattern in the errors, and to determine the reasons for these errors.
- Naive Single Prediction Tests: This method aims to assess whether the model can make an accurate prediction or not. To understand this, it is tested through a simple example. It is important to note, however, the probabilistic nature of ML makes this testing method not functional as it is. It should be supplemented with other methods.
- Minimum Functionality Test: This procedure can be appealed to address the shortcomings of the methods above. This method allows the evaluation of specific components of an ML model instead of presenting a panoramic view.
- Invariance Tests: By using this method, model developers can determine to what extent a change in the input affects the output. Therefore, through this testing procedure, it is intended to create an ML model that is blind to the correct variables.
If you have other questions about model testing, we can help:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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