Machine learning is one of the front running technologies in the world that has grabbed a lot of eyes. It has penetrated almost every industry and been on the top of the list of researchers and scientists. Be it business, healthcare, climate change or other studies, machine learning is finding its way everywhere. The programming language Python became 2019’s most widely used language, beating JAVA in the race. One of the reasons behind this remains the popularity of machine learning in the world and Python just proving as the quintessential tool to help in its implementation.

Wide Application of Machine Learning

For businesses, machine learning has been the answer to a lot of complex problems. For example, understanding the customer’s behavior by analyzing their purchase trends is now a seamless task using machine learning. Similarly, getting an idea of the products that a customer is likely to purchase in the future is now also possible with predictive machine learning models.

In the healthcare sector, machine learning is paving way for personalized medicine that will be given to a patient based on the information about their genetic history, past treatments etc. In climate change studies, predictive models are playing a huge role in determining the levels of pollutants or rise in sea level in the future. It is not just helping scientists understand climate change better but also come with strategies to battle it efficiently.

All of this is just a beginning. No matter where you look, you can see machine learning aid the decision-making process and help come up with more streamlined and cost-saving strategies. But, as we progress into the future, machine learning models are becoming more and more complex. Researchers are not just training machines but adding different layers of networks to pass the data that result in meaningful outcomes. Neural networks are just one example of this and many other such models follow in machine learning.

Machine Learning and Testing

However, as we keep on developing varied models on different architectures, it is imperative to analyze their quality. We can understand this in the context of new software. Let’s say that you have built new application software for your client that helps them ship products on its own. You have finished the coding stage and compiled all the models carefully. The compilation gave no errors and your software is ready. What do you do next? Do you hand it over to the user? Or do you test it first? Analyzing the quality of your software is a must, which is why it is an indispensable part of the software development life cycle.

A similar scenario applies to machine learning but sounds a bit twisted at first. Machine learning models are all about getting the results, so what is the point of worrying about their quality. For a long time, people have been of the view that there is no point testing a product that uses machine learning algorithms.

All one needs to make sure is that it is giving accurate results. But this approach is paradoxical, which is why most organization has no clue on how to perform quality analysis on ML models. The point is that with the expansion of AI and ML, everyone is reluctant to incorporate them into their products, without knowing how to handle the QA aspects.

This practice leaves a lot of genuine and application critical questions unanswered. Some of these include:

• How to ensure that the accuracy of a model is preserved while production?
• When a new change is released, how to make sure that the existing model in production does not collapse?
• How to perform tests on the model to make sure that previously developed and tested software still perform after a change?

To begin answering these questions, we need to first understand the aspects of a machine learning model that can be tested or analyzed for quality. This step ensures that a well-structured approach is adapted to testing ML models, that doesn’t require the intervention of a data scientist and can be performed successfully by the QA team. Here’s what can be tested within an ML model:

• Quality of data: It helps in testing data for any data poisoning attacks
• Quality of features: It helps in determining the features and their relationships
• Quality of algorithms: It ultimately helps in determining the performance of an algorithm.

Data

Organizations often ignore this aspect, but checking the quality of data is crucial to determining the success of your ML model. It should be made sure that the data being used for training and validating the model is sanitized. Under no circumstances should the data belong to an adversary data set. In other words, adversary sets are those sets of data that can skew the results of an ML model by letting it train on incorrect data. It is also widely known as data poisoning attacks.

To assure the quality of data, analysis is a must. QA teams must put test mechanisms in place to validate the data used for training is sanitized. To accomplish this, software testing services teams can work in collaboration with product teams and try to understand key statistics from the data. Some of these may include:

• Mean, median, mode, etc. of the data
• The relationship between the data such as variance, correlation etc.

Once these are identified, tests can be built to check and validate the statistics and relationships.

Features

Features are an important part of an ML model. However, at times many of these may become irrelevant to the purpose of the experiment. Leaving these as it is in the model can lead to increased error rates. Therefore, QA analysts must use being feature engineering into the picture and use techniques like feature selection, dimensionality reduction etc. Some of the key aspects that must be tested for the quality assurance of features are feature thresholds, relevance, relationship, suitability, compliance, testing and review. Based on these aspects, quality analysts can determine the necessity and relevance of a feature for the model.

Algorithms

As ML models get trained over and over again, any increase in the error rate results in the revaluation of the ML algorithm. Organizations can try using different algorithms and keep the ones that result in the lowest error rates. They can also retrain the model and try evaluating the performance. Mostly the efficiency of an algorithm comes down to bias-variance tradeoff. If the variance of your model is high, it will lead to overfitting and increase the sample size. Similarly, if the bias is too high, it leads to under fitting and means that there are too few features being used. Based on your applications, it is essential to strike a balance between bias and variance.

Conclusion

Quality analysis is as fundamental to machine learning models as it is for application software. Organizations must initiate a step by step process of quality assurance that begins from the data and ends on the algorithm. A collaborative effort between the QA teams and product developers will go a long way in accomplishing the task.