What are your best practices for measuring success in Machine Learning?

Machine learning (ML) is a powerful and versatile tool for solving complex problems and creating value from data. However, to ensure that your ML projects are successful, you need to define and measure the right metrics that align with your goals and expectations. In this article, we will share some of the best practices for measuring success in ML, covering topics such as choosing appropriate evaluation methods, setting baselines and benchmarks, monitoring performance and feedback, and communicating results and insights.

1 Evaluation methods

Depending on the type and purpose of your ML model, you may need to use different evaluation methods to assess its accuracy, reliability, and usefulness. For example, if you are building a supervised learning model that predicts a categorical outcome, you may use metrics such as precision, recall, F1-score, or confusion matrix. If you are building an unsupervised learning model that clusters data into groups, you may use metrics such as silhouette score, Davies-Bouldin index, or Calinski-Harabasz index. You should also consider the trade-offs and limitations of each metric, and use multiple metrics to get a comprehensive view of your model's performance.

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Jeeone Park

EZL Business Solutions
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Note that the evaluation metric you have selected may not fully or exclusively represent the performance of a model. For example, assume a segmentation model is being evaluated with Dice coefficient. Result of the evaluation is highly dependent on the size of the object (or the background), which results in imbalance of error values. Easily said: 1 pixel error of 2 pixel object in 100 pixel image results in Dice of 66%, but 1 pixel error of 98 pixel background gives Dice of ~99%. Therefore, evaluation methods should be designed carefully to best represent the performance depending on specific task being performed and also the data characteristics.

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Priya Ranjani Mohan

Manager, Cybersecurity & Tech Risk @ KPMG || Author of Linkedin Learning AI Courses || Forbes Technology Council Member || Speaker || AI Strategist
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Supervised Learning Evaluation Metrics: Accuracy: Measures the proportion of correctly classified instances among all instances. Precision: Measures the ratio of correctly predicted positive observations to the total predicted positives. Recall (Sensitivity): Measures the ratio of correctly predicted positive observations to the actual positives. F1-Score: Balances precision and recall, giving you a harmonic mean of the two. ROC Curve (Receiver Operating Characteristic): Plots the true positive rate against the false positive rate, showing the trade-off between sensitivity and specificity. Log Loss (Cross-Entropy Loss): Measures how far your predicted probabilities are from the actual classes

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2 Baselines and benchmarks

Before you start training and testing your ML model, you should establish a baseline and a benchmark for comparison. A baseline is a simple or naive model that serves as a lower bound for your model's performance. For example, a baseline could be a random guess, a constant value, or a simple rule-based classifier. A benchmark is a more advanced or state-of-the-art model that serves as an upper bound or a target for your model's performance. For example, a benchmark could be a published result, a previous model, or a human expert. By comparing your model with the baseline and the benchmark, you can measure how much improvement your model achieves and how close it is to the optimal solution.

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Gordon Böer

Machine Learning Engineer @ IBAK
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Regarding the performance evaluation using benchmarks, it's crucial to recognize the dynamic nature of the ML field. Today's cutting-edge solution might be rendered basic tomorrow, emphasizing the need for continuous benchmarking. While benchmarks provide direction and motivation, over-relying on them can be perilous. A model that's hyper-optimized to surpass a benchmark might not necessarily translate to superior real-world performance, as it might be overfitted to specific data or scenarios. Therefore, while benchmarks are valuable, they should be used judiciously, always keeping in mind the broader application and real-world implications of the model.

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3 Performance monitoring and feedback

Once you deploy your ML model into production, you should monitor its performance and feedback over time. This is because your model may face changes in the data distribution, the user behavior, or the environment that affect its accuracy and relevance. For example, you may encounter concept drift, where the relationship between the input and output variables changes over time, or data drift, where the statistical properties of the input data change over time. To detect and address these issues, you should collect and analyze data on your model's predictions, errors, and outcomes, and use feedback loops to update and retrain your model as needed.

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4 Communication and insights

Finally, you should communicate your ML model's results and insights to the relevant stakeholders and audiences. This is because your model's success depends not only on its technical performance, but also on its business impact, user satisfaction, and ethical implications. To communicate effectively, you should use clear and concise language, visualizations, and stories that explain your model's goals, methods, metrics, and outcomes. You should also highlight the value and benefits of your model, as well as the limitations and challenges. Moreover, you should solicit and incorporate feedback from your stakeholders and users, and use it to improve your model and its communication.

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Jeeone Park

EZL Business Solutions
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Human interpretation should be the first and final step in evaluating the ML/DL model. There are a lot of occassions where quantitative data does not portray critical limitations of a model or data population. Also, most of the stakeholders may not have experience in ML/DL field and thus require thorough interpretation of results. This is especially important in the medical AI industries since erroneous clinical decisions pose potential hazards to patients and possibly death.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are your best practices for measuring success in Machine Learning?

1

2

3

4

5

1 Evaluation methods

2 Baselines and benchmarks

3 Performance monitoring and feedback

4 Communication and insights

5 Here’s what else to consider

Machine Learning

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What are your best practices for measuring success in Machine Learning?

1

2

3

4

5

1 Evaluation methods

2 Baselines and benchmarks

3 Performance monitoring and feedback

4 Communication and insights

5 Here’s what else to consider

Machine Learning

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