Machine learning (ML) is a study of applying algorithms, behavioral data sets, and statistics to make a system learn by itself 📊 As these systems will not have any external help, ensuring they are robust and perform as expected is imperative.
Testing is, therefore, a vital element in the development of these systems, though it can be trickier than a traditional software approach. Here, we’ll go through the objectives of testing, how model tests should be written and some good tests to apply to ML systems.
Machine Learning Testing for Beginners – Table of Content
- What Is the Objective of Machine Learning Testing?
- How Should Model Tests Be Written?
- All You Need to Know About Model Development Pipeline
- Enhancing Machine Learning with Testing
- Different Testing Techniques in Machine Learning
- Leveraging the Best Data for Machine Learning Testing
- Final Takeaway
Before jumping into machine learning testing, we have to ask an important question, what are we trying to achieve when performing ML or software tests?
Quality assurance is necessary to ensure that the software system meets your requirements. Here, we check if all the agreed-upon features have been implemented and if the program behaves as expected. When testing the program, all the parameters used should be listed in the technical specification document 📃
Detect Bugs and Flaws
You don’t want unhappy clients coming back to you with a list of problems. Software testing can detect any errors and vulnerabilities in the code during development. Additionally, different kinds of testing can catch bugs that are only visible at runtime.
Like the difference between traditional phone systems and cloud PBX, traditional and ML testing have key differences in how they function.
Traditional software testing is human-driven, with programmers providing the input data and logic. The machine validates the logic and checks for the desired behavior of the system or program 👩💻
When it comes to testing for ML, programmers input data and the desired behavior to produce the logic of the machine. It’s tested repeatedly to see if the learned logic remains consistent. If it does, it ensures that the system understands the logic and develops a model according to the desired behavior.
A testing model summarizes how you should think about test development. When writing model tests, several issues need to be considered:
- Testing the basic logic of the model
- Managing the model performance by using manual testing
- Evaluating the accuracy of the ML model
- Make sure that the achieved loss is acceptable for your task
- Checking model performance on real data
Another consideration is continuous testing. Building continuous testing procedures into your model testing strategy will give faster delivery and feedback to developers. There are different types of tests that you can use 👇
Pre-train tests allow you to catch bugs before running the model. This type of test is performed early on and doesn’t need training parameters to run. Its main objective is to avoid wasted training jobs.
Some examples of tests include:
- Checking if any labels are missing in your training and validation datasets
- Check the single gradient to find the loss of data
Post-train tests deal with job behavior. They are carried out on a trained model and check whether it performs correctly ✅ The main purpose here is to investigate the logic behind the algorithm and detect any bugs that exist. There are three types of tests that can be used to report the behavior of the program:
- Invariant test– This testing technique allows us to check how much we can change the input data without affecting the performance of the machine learning model. We can use paired input examples (original and changed) and check for consistency in the model predictions.
If the trained model performs correctly, the slight changes in the input should not affect the model predictions.For example, we can carry out a pattern recognition model on two pictures of oranges. We wouldn’t expect the result to change between the two.
- Minimum functionality test– Minimum functionality tests are carried out similarly to unit tests in traditional software testing. The components of the program are split up, and testing is applied over those components. Minimum functionality testing allows you to assess the model performance based on specific cases found in your data. This allows you to identify critical instances where prediction errors can have serious consequences.
- Directional test– Unlike invariant tests, directional testing checks how perturbations in the input change the model behavior. If the trained model performs correctly, the changes in input will affect the model prediction. For example, if we had a model that estimates the price of a house, taking into account square footage, we would want to see that added space makes the house prices go up.
Unlike invariant tests, directional testing checks how perturbations in the input change the model behavior. If the trained model performs correctly, the changes in input will affect the model prediction.
For example, if we had a model that estimates the price of a house, taking into account square footage, we would want to see that added space makes the house prices go up.
With machine learning, the concept of pipelining is used to automate workflows. ML pipelines are iterative. Repeating one procedure after another improves the algorithm’s accuracy and achieves the required solution.
Traditional ML model development can have slow iteration cycles, due to manual and script-driven processes. With an automated ML pipeline, fast iteration cycles shorten the time between training models and deploying them 🔂
Model testing has several benefits, such as easy maintenance, lower costs, early detection, and taking less time. There are a few considerations to make when building an ML pipeline:
- Codify tests into components: testing is an inherent part of the model development pipeline and it’s faster with automation. Ensure you include the same tests you would use in manual testing, such as ad hoc testing, for an automated pipeline.
- Tie your steps together: in an ML pipeline, you define the order in which components are executed and how inputs and outputs run through the pipeline.
- Automate when needed: building a pipeline introduces automation and manages the running of subsequent tasks. You might want to automatically run an ML pipeline when specific criteria are met. For example, you may monitor model drift to trigger a re-training run automatically.
The following steps are involved in the model development pipeline:
- Pre-train test
- Post-train test
- Train model
- Model evaluation
- Dataset review and approval
An ML library is a compilation of readily available functions and routines. It enables developers to research and write complex programs without having to personally write all the code.
While ML libraries for modeling are well-tested, they’re not perfect. Testing can enhance ML by ensuring the system works as expected when code is integrated from the library, improving the quality of the models.
Test cases are used to determine if the system being tested satisfies requirements and works correctly. The testing procedure ends when all the functional and non-functional requirements of the product are fulfilled.
When executing a test case, you need to deal with five test case parameters:
- The product’s initial state or preconditions– This is the required state of a test item and its environment before test case execution.
- Data organization– This involves selecting data from existing databases or creating, generating, and editing data for testing. This data must be high quality to produce high-quality results.
- Dataset for input– This is the data required for test execution.
- Forecasted output– This is the predicted behavior of a model and what it should predict.
- Expected output– This parameter is the observable predicted behavior of a test item, under specified conditions.
Regression testing covers already tested software to ensure it doesn’t suddenly break, even after a change of component or module 🔎 For example, retesting a dialer after making a feature upgrade. If the software didn’t work after several modifications, then it would be called a regression.
Here, the program is split into blocks, or units, and each section is tested separately. Each unit is called and validated to ensure the individual components of the model meet the user requirements.
Beta testing, or usability testing, gives selected target users an almost finished version of the program. It helps finding bugs and is carried out to match and validate the program with the user’s requirements.
Beta tests are typically deployed several times to achieve this. For example, giving select users access to new versions of video conferencing platforms, like Jitsi or an alternative to Zoom.
This type of testing is done just before the product launches. It’s similar to beta testing, where users test the program. But this time it’s done in-house with the testing team. Alpha testing aims to find and fix bugs that weren’t discovered through previous tests 👀
In integration testing, the result set is taken from unit testing and groups of modules combined to see how they work together. The main purpose of integration testing is to ensure that modules interact correctly when combined and that the standards of the system and model are met.
For example, consider a UCaaS model that combines multiple communication channels in an app. An UCaaS provider will test each channel on its own and with other channels. If there were issues when combined, the system wouldn’t be fit for the user’s need.
ML testing is necessary to develop a model that performs how it’s expected. Just like traditional software testing, vulnerabilities can cause chaos and damage reputations. For example, a vulnerability in cybersecurity automation could spell disaster for the model.
Thorough testing eliminates bugs and errors that would otherwise frustrate end users and ensures the final product is to the user’s requirements.
But, ML doesn’t just care about models. ML models produce predictions based on input data. So, the quality of the input data will directly impact the quality of model predictions. Therefore, the data used to train the model must be of high quality, to ensure the best possible predictions.
Test the quality of your data. Consider the various tests available and go through several iterations of testing to ensure seamless and high performance from your ML model 💪
To summarize: a machine learning system is a powerful tool for efficiency and the proper maintenance of it is a must. Machine learning testing helps companies ensure that their software systems are meeting the desired quality, detecting bugs and flaws easier so that they can be quickly dealt with.
Good testing starts with a properly written model test that fits your specific needs, putting into consideration the model development pipeline. Meanwhile, the different testing techniques available target different levels in the testing pyramid, allowing you to target and test specific points.
Be keen on the data you feed into the system — it’ll affect the performance of your model down the line! ✨