Why Machine Learning Accuracy Matters and Top Tools to Supercharge It

Over 40% of organizations are still stuck in the early stages of ML adoption, despite proven business benefits. A major cause of this is concerns around ML accuracy. In this article, we discuss the various benefits of having accurate machine learning models in place and look at three useful tools that can get you over the line. 

Machine learning holds enormous potential for organizations, from identifying security risks via video surveillance to predicting business outcomes. However, research suggests that the adoption of machine learning is languishing at the early stages of maturity due to accuracy concerns. According to Algorithmia’s 2020 State of Enterprise ML reportOpens a new window , 22% of companies are early stage adopters with models in production for less than two years. Another 21% are only beginning to evaluate ML use cases. Meanwhile, the business benefits of ML are increasingly apparent. 37% of those surveyed for Algorithmia’s report said they want to use ML for customer intelligence, 38% want to reduce costs, and 30% are looking to automate internal processes through ML technology. 

Companies must achieve the ML accuracy benchmarks needed to become mature adopters and to unlock the desired business benefits. 

Also, there can be severe fallouts if an inaccurate ML model goes live without being detected. Consider the series of ML algorithms developed by Epic, one of the largest electronic health record vendors in the U.S. Recent investigations revealed that the ML was delivering information that was either inaccurate or not relevant for clinicians, particularly when predicting the onset of sepsis. It missed out on identifying two-thirdsOpens a new window of cases, and the clinician would have to attend 109 possible sepsis diagnoses to identify a single valid patient. 

In other words, the stakes are high. It is vital to focus on producing effective ML algorithms that can predict real-world scenarios accurately and reliably.

What is ML Accuracy? The Answer Is Nuanced

What exactly do we mean when we use the term “ML accuracy?” Machine learning algorithms are designed to solve real-world business problems with the same (or more) efficacy as human judgment. For instance, if a human machining expert can anticipate the potential shelf life of a piece of equipment, you should be able to predict machine failure and wear-and-tear with greater accuracy using ML. Therefore, ML accuracy refers to a model’s ability to get as close to the desired outcome as possible with minimal deviance. 

In this context, there are three ways to look at ML accuracy: 

A simple definition of accuracy: ML accuracy can be measured as a percentage of success, where the number of correct predictions or classifications significantly outnumbers incorrect ones. For instance, a visioning ML model that identifies 95% of images as dogs can be considered to be highly accurate. 

Optimizing accuracy with “precision”: ML outcomes may also include false positives – like if a sphynx cat is mistakenly identified as a dog. Precision refers toOpens a new window the number of correct positives divided by the total number of positives, true and false included. Essentially, it tells you how accurate your accuracy rate is. 

Digging deeper with “recall”: Just like you can have false positives, you can have false negatives as well – like if a terrier dog breed is mistakenly identified as a cat. Recall is calculated by dividing the number of true positives by the sum of true positives and false negatives. It tells you how accurately the ML model was able to recall the real-world scenario. 

These definitions and nuances are important because otherwise, companies risk getting bogged down by a simplistic ML accuracy number that isn’t relevant to the business problem at hand. Keeping this in mind, there are a number of tools you can use to increase the accuracy levels of your machine learning models. 

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3 Tools for Improving ML Model Accuracy 

As our understanding of ML evolves, so do the concerns around accuracy. ML versioning – a key parameter for model monitoring and accuracy checks – was a problem for 24% of companies in 2018. By 2020, this number increased to 41%. Writing ML monitoring code and scripts for accuracy measurements from scratch can be a difficult task. Fortunately, there are several tools you can use to address this. 

1. Amazon SageMaker Model Monitor 

SageMaker is a cloud-based ML development platform launched by Amazon in 2017. SageMaker’s Model Monitor service, in particular, is designed to help you automatically detect inaccurate predictions that are thrown up by ML models in production. 

Key features include: 

• ML metadata collection, monitoring, and encryption 
• Bulti-in analysis via statistical rules to identify drifts in model quality 
• Data visualization in SagMaker Studio and scheduled monitoring
• Updating data with new real-world observations 

Ideal for: Predictive models fed with real-world information, like the weather. 

Pricing: SageMaker is priced by the hour for the specific features you use, starting at around $0.05 per hour per service. 

2. Augur.ai 

Augur is a dedicated product for predictive ML model accuracy and ROI calculation. Apart from monitoring ML mode training to control drift, it also has products for ML development automation and open-source pipelines. 

Key features include: 

• Visualization of model drift as changes in actual values vs. predicted values 
• Revenue and investment configuration for ML events to calculate ROI 
• Works with virtually any ML platform (unlike SageMaker Model Monitor, which is limited to AWS) 
• Statistical analytics engine and real-time diagnostic charts 

Ideal for: ML for business scenarios like cost-benefit analysis.

Pricing: Augur is available at $200 per month for accuracy checking for up to 1000 predictions vs. actuals. 

3. Orange Data Mining Widgets 

Orange provides open-source tools for machine learning and data visualization. It is a community of developers that builds integration-friendly tools and conducts workshops to help in game-changing ML innovation. Orange has a dedicated widget for testing the accuracy of ML classification algorithms. 

Key features include: 

• Supports different performance measures like classification accuracy and “area under the curve” 
• Various sampling methods supported, including cross-validation and random sampling 
• Pairwise comparison of ML models 
• Visualization through simple props and charts 

Ideal for: ML classification algorithms, especially for independent developers, research, and open-source.

Pricing: Orange Data Mining Widgets are available for free. 

Apart from this, there are tools like Neptune.ai and Intel OpenVINO that help in the other aspects of ML development, which has an overall impact on model accuracy. 

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Making ML More Accurate: A Strategic Imperative 

At the end of the day, ML model accuracy is not only a technical conversation. Your definition of accuracy and how you achieve it will differ from industry to industry, from one use case to another, and the specific model types in question. For instance, 95% accuracy may not be sufficient for a national security application, but a large number of false positives may be permissible. That’s why it is important to converge technical KPIs with business goals to help build an ML model that works for you. 

Do you agree that greater accuracy is the secret to wider ML adoption and success? Comment with your thoughts below or tell us on LinkedInOpens a new window , TwitterOpens a new window , or FacebookOpens a new window . We would love to hear from you!