What Are The Best Ways To Visualise Machine Learning Algorithms

Key takeaways

Machine learning is transforming the IT industry and influencing everyday life significantly.
The availability of high-quality data is crucial for effective machine learning projects.
Visual analytics can enhance understanding and interpretation of machine learning algorithms.
The combined use of complementary technologies complicates data handling for analysts.
Analytical skills are essential to navigate the complexities of modern data environments.

Machine learning and data science are revolutionising the information technology industry and the way innovations are impacting our lives. With so much going on around these areas, it is often difficult to assimilate ideas and concepts around them. Additionally, the growing number of tools are overwhelming the these areas. This article discusses a visual mindset towards machine learning, that allows us to gain the best from the subject.

Making Best Use Of The Omnipresent Data

One of the key factors that ML is dependent on, is the availability of right data for a project. In today’s age, data is everywhere and forms the lifeline of any business. Data analysts have realised this potential and have been leveraging useful data to achieve better results in terms of performance. With other complementing technologies such as big data, cloud services, data mining and many more, data has become complicated to work with. One needs the right set of analytical skills.

Is ML Better Understood Through Visual Analytics

In a study by academics at Tsinghua University, China, researchers worked with a concept called interactive model analysis, where ML is understood better through visual analytics. The authors highlighted the obscurity present when users work with ML. To resolve this, they made a thorough analysis with interpretation and categorised their work into three stages:

Understanding
Diagnosis
Refinement

The researchers first obtained data from a typical machine learning pipeline. Then they extracted features that could be used as input to a machine learning model. “Next, the model is trained, tested, and gradually refined based on the evaluation results and experience of machine learning experts, a process that is both time consuming and uncertain in building a reliable model,” the authors said. “In addition to an explosion of research on better understanding of learning results, researchers have paid increasing attention to leveraging interactive visualisations to better understand and iteratively improve a machine learning model. The main goal of such research is to reduce human effort when training a reliable and accurate model. We refer to the aforementioned iterative and progressive process as interactive model analysis”, they added. They analysed the current methods in machine learning and provided the visual cues of why ML models behave that way. The work also entails collecting insights from other ML models and achieving a better performance across these models. The ‘interactive model analysis’ will therefore serve as a primer for anyone interested to develop a more visual ML in the future. However, authors only concern was that the visual framework would lead to uncertainties both on part of the machines as well as from humans.

Visual Interpretation

Another aspect to ponder in picturing ML is the way models are converted into visual depictions. A typical table or chart might not show the dynamics of machine learning in the best way. An article by Ganes Kesari, illustrates a visual framework which has four key elements to depict ML. The framework relies on the ML insights from a project. The four elements which make up the framework are:

Information Design: Statistical interpretations should be visually appealing with better design and user interface.
Adaptive Abstraction: Simplifying the level of complexity and details involved in the project is the focus of the element.
Model Unravelling: Concepts such as Neural networks, are still difficult to ascertain. Models incorporating these concepts should bring out better traceability keeping the information flow simple and easy to understand.
User Interactivity: The way users interact with ML models depend on the plethora of innovative features infused in the ML project. The more interesting the appeal is, the more users find it comfortable to work with.

These elements form the aesthetic of ML and to capture more user attention for any ML project, it should necessarily benefit from the above method.

Conclusion

The art of visualising ML in depth is still in initial stages. Nevertheless, the research towards bringing visual ML methods is catching up lately and is fascinating along the way. One needs to make sure that ML aspects are kept intact, without compromising the understanding and its working. In addition, user experience should also be kept in mind in the pursuit of ML design.