What Is Machine Learning Testing? 

Machine learning testing is a phase in the development of machine learning (ML) models that focuses on evaluating their performance, reliability, and fairness. Unlike traditional software testing, ML testing deals with models that learn from new (not previously trained upon) data and can change their behavior based on the inputs they receive.

This makes the testing process more complex, as it needs to account for various scenarios the model might encounter in real-world applications. By thoroughly testing ML models, developers can build reliable systems that deliver consistent and accurate results.

ML testing involves a series of assessments to identify and mitigate errors, biases, and other issues that could compromise the model’s effectiveness. Techniques such as A/B testing, where different models are compared in live scenarios, are commonly used. The goal is to identify and minimize issues that could affect the model’s intended performance, ensuring it meets the desired standards and behaves predictably in real-world situations.

Why Is ML Model Testing So Important? 

Here are a few ways machine learning model testing can benefit organizations developing or deploying AI technology.

Evaluation of Model Performance

Evaluating model performance involves assessing how well an ML model accomplishes its intended tasks across a range of conditions. Performance metrics such as accuracy, precision, recall, F1 score, and area under the ROC curve are commonly used to quantify a model’s effectiveness. However, a single aggregate metric is often insufficient to capture the nuances of model performance.

Evaluation requires using a diverse set of test data that reflects the variety of real-world scenarios the model will encounter. This includes different data distributions, edge cases, and potential adversarial examples. Continuous monitoring and refinement of the test data ensure that the model adapts and maintains high performance for new data and situations.

Learn more in our detailed guide to ML model evaluation 

Validation of Assumptions and Data Sources

Every ML model is built on a set of assumptions about the data it will process and the environment in which it will operate. These assumptions can range from the distribution of input data to the relationships between different features. Validating these assumptions involves checking whether they hold true in real-world applications. This helps prevent model failures due to incorrect or unrealistic assumptions.

Additionally, the quality and reliability of the data sources used to train and test the model must be verified. Data integrity checks ensure that the data is accurate, complete, and relevant to the problem at hand. This step is crucial because the model’s performance and generalizability depend on the quality of the data it learns from.

Ensuring Model Fairness and Reducing Bias

ML models are increasingly used in high-stakes decisions affecting individuals and society. Fairness in ML means that the model provides equitable outcomes across different demographic groups, such as race, gender, or age, and does not disproportionately favor or disadvantage any segment.

To achieve this, the model must be tested with diverse datasets that represent the variety of populations it will encounter in the real world. This includes reweighting training data, modifying the learning algorithm, or adjusting the decision threshold to balance outcomes across groups. Regular audits and fairness checks should be integrated into the testing pipeline to ensure ongoing compliance with fairness standards.

Verifying System Reliability

It’s important to ensure that models can perform consistently under various conditions, including unexpected scenarios and edge cases. An ML model must be able to handle diverse and potentially noisy input data without performance degradation. It should also be reliable, ensuring consistent performance over time and across different environments.

This verification process involves stress testing, where the model is subjected to extreme conditions to observe how it reacts. Scenario-based testing is used to evaluate the model’s performance in relevant situations. Techniques like adversarial testing can identify vulnerabilities by intentionally introducing perturbations to the input data.

Why Is ML Testing Hard? 

Machine learning testing presents several challenges compared to traditional software testing.

1. Manual and Haphazard Testing Practices

Current ML testing practices are often manual and lack standardization, relying on the domain knowledge and intuition of engineers. This approach is time-consuming and prone to human error, leading to inefficient testing processes. Engineers might spend up to 80% of their time on testing and validating models, and even with this effort, performance gaps are often discovered once models are deployed.

2. Diverse Data Distributions and Edge Cases

ML models must be tested against a variety of data distributions and edge cases to ensure accuracy and consistency. However, creating test datasets that cover all potential real-world scenarios is challenging. Models need to be stress-tested under extreme conditions and exposed to adversarial examples to identify vulnerabilities. This requires an understanding of the possible variations and anomalies in the data.

3. Hidden Stratification Phenomenon

Aggregate performance metrics, like overall accuracy, can hide poor performance on critical sub-tasks or scenarios. A model might show high accuracy on average but fail in specific, essential cases.

These silent regressions can lead to overestimations of the model’s capabilities, as improvements in trivial scenarios mask deteriorations in more critical ones. Regular updates with new data can exacerbate this issue, because the new data might have unexpected impacts on model performance.

Redefining ML Testing: Towards ML Unit Testing 

To address the complexities of machine learning testing, it’s essential to move towards a more systematic approach akin to traditional software testing. The concept of unit testing, which is a foundation of traditional software development, can be useful for standardizing and managing ML testing as well. Unit testing in ML involves breaking down the model’s performance into separate, manageable components and rigorously evaluating each part.

The Importance of Systematic ML Unit Testing

Systematic unit testing of machine learning models helps overcome the limitations of traditional evaluation techniques, which often rely on aggregate metrics like accuracy or F1 score. These metrics provide a broad overview but can obscure areas where the model underperforms (the hidden stratification problem).

By focusing on smaller, well-defined test cases, developers can gain a more detailed understanding of model behavior. For example, Twitter’s racially biased cropping algorithm tended to favor white faces over black faces. This bias was not apparent through overall accuracy but was evident when specific scenarios were tested. By implementing unit testing, such biases can be identified and addressed before deployment.

Implementing ML Unit Testing

To implement ML unit testing, developers need to adopt several concepts from software engineering:

• Unit test: Tests a specific function or feature of the model in isolation.
• Test case: A collection of data samples representing a specific scenario.
• Test suite: A group of related test cases aimed at achieving a broader testing objective.
• Smoke testing: Quick, preliminary tests to ensure critical functionalities are working.

For example, in a car detection model, the “car” class can be broken down into multiple test cases based on attributes such as lighting conditions, occlusions, and different types of cars. This granular testing approach allows for more precise identification of failure modes.

Benefits of ML Unit Testing

ML unit testing can provide the following benefits:

• Granular understanding of model behavior: Developers can pinpoint exact failure modes and areas needing improvement.
• Reduced experimentation efforts: By identifying issues, teams can focus their efforts more efficiently during model training and data collection.
• Improved documentation and knowledge sharing: Well-defined unit tests serve as documentation for the model’s success criteria, enabling better knowledge transfer within the team.

The ML Testing Process 

Here’s a general process you can use to test ML models, while adopting the unit testing approach.

Step 1: Curating High-Fidelity Test Data

Properly curated test data is essential for ensuring the reliability and trustworthiness of machine learning models. Managing test data can be more crucial than managing training data because the accuracy and performance of the model’s responses are evaluated based on test scenarios.

A well-managed test dataset provides a clear understanding of the model’s behavior before deployment, ensuring that the model performs as expected in real-world situations. This approach allows for evaluating the model’s performance across various categories, such as factual accuracy, bias, data leaks, and prompt injections.

By implementing unit tests, as described in the previous section, companies can ask precise questions to gauge the model’s capabilities in critical scenarios. For example, testing for biases can reveal discrepancies in how the model treats different demographic groups, while scenario-specific tests can assess the model’s resilience to rare edge cases.

Another aspect is balanced distribution of test data, which is crucial for accurate performance evaluation. If the test data is skewed towards less critical tasks where the model performs well, it can mask deficiencies in more important areas. Testers must ensure that test datasets are representative and include diverse scenarios.

Step 2: Standardizing the Quality Assurance Process

This step involves standardizing the following elements:

• Test coverage: Ensures that all identified risks are tracked and evaluated throughout the ML development cycle. This requires clear communication within the team and across the organization to build a knowledge base of key testing scenarios and risks.
• Metrics: Common ML metrics, such as precision, recall, and F1 score, may not always provide a complete picture of a model’s performance at the product level. Custom product-level metrics should be established to reflect the goals of the application. For example, a “collision risk” metric for a robotics system or an “expected revenue” metric for a recommender system can provide relevant insights into the model’s effectiveness.
• Pass/fail criteria: Clearly defined success criteria are essential for consistent and objective evaluation of ML models. These criteria can include weighted importance scores, thresholds for acceptable performance, or no regression requirements between model updates.

Step 3: Testing at the Product Level

Here are some considerations for this step:

• End-to-end testing: ML models often function within larger systems, requiring comprehensive testing to ensure reliable performance across the entire pipeline. This involves evaluating how well different models and processes interact and perform collectively, rather than assessing individual models in isolation. The right combination of tests yields the best product-level results. For example, a model used in an autonomous vehicle system must be tested for its object detection capabilities as well as its integration with navigation, control systems, and real-time data processing units.
• Regression testing: By continuously testing the model against previously identified scenarios and criteria, developers can ensure that new updates or changes do not negatively impact its performance. Regular regression testing also builds confidence among stakeholders that the model remains reliable, preventing unexpected failures that could disrupt operations or degrade user trust.
• Product-level metrics: These should drive end-to-end testing efforts, focusing on outcomes relevant to the entire system rather than individual model performance. For example, instead of solely focusing on the accuracy of a language translation model, product-level metrics might assess the overall user satisfaction with the translation service, including factors like fluency, context preservation, and response time.

By following this process, you can achieve a standardized ML testing process that results in reliable, unbiased machine learning models that meet user expectations.

Testing and Evaluating ML Models with Kolena

We built Kolena to make robust and systematic ML testing easy and accessible for all organizations. With Kolena, machine learning engineers and data scientists can uncover hidden machine learning model behaviors, easily identify gaps in the test data coverage, and truly learn where and why a model is underperforming, all in minutes not weeks. Kolena’s AI / ML model testing and validation solution helps developers build safe, reliable, and fair systems by allowing companies to instantly stitch together razor-sharp test cases from their data sets, enabling them to scrutinize AI/ML models in the precise scenarios those models will be unleashed upon the real world. Kolena platform transforms the current nature of AI development from experimental into an engineering discipline that can be trusted and automated.

Among its many capabilities, Kolena also helps with feature importance evaluation, and allows auto-tagging features. It can also display the distribution of various features in your datasets.

Reach out to us to learn how the Kolena platform can help build a culture of AI quality for your team.