How do you use data visualization to communicate uncertainty and confidence in your ML models?

1 Why uncertainty and confidence matter

Uncertainty and confidence are not the same thing, but they are related. Uncertainty is the degree of doubt or variability in your model or data, while confidence is the degree of trust or belief in your model or data. Uncertainty can arise from different sources, such as noise, bias, missing values, outliers, or model complexity. Confidence can depend on factors such as sample size, data quality, model performance, or validation methods. Communicating uncertainty and confidence can help you and your audience understand the limitations and strengths of your ML models, and make informed decisions based on the available evidence.

2 How to measure uncertainty and confidence

When assessing the uncertainty and confidence of ML models, there are various methods to consider. For instance, confidence intervals are ranges of values that contain the true parameter or prediction with a certain probability. These intervals indicate the precision and variability of estimates, and can be visualized with error bars, bands, or boxes. Prediction intervals are similar in that they display the future observation or outcome with a certain probability. Probability distributions are functions that describe the likelihood of different values or outcomes, such as normal, binomial, or Poisson distributions. Lastly, Bayesian methods use prior knowledge and data to update the beliefs or probabilities of different parameters or predictions. These methods indicate the uncertainty and confidence of your model or data, and can be visualized with posterior distributions, credible intervals, or decision trees.

3 How to choose the right visualization

Choosing the right visualization for your uncertainty and confidence is essential for conveying the main message and avoiding misinterpretation. Consider the type and range of your data or model when selecting scales and formats; for instance, logarithmic scales for exponential data or model, or percentages or fractions for proportions or probabilities. Colors, shapes, and symbols should be used to highlight the uncertainty and confidence of your data or model. Additionally, labels, legends, and annotations should explain the meaning and source of your uncertainty and confidence. Titles or captions can be used to state the level of confidence or probability, while tooltips or pop-ups can show the details or calculations of your uncertainty or confidence.

4 How to test and improve your visualization

Testing and improving your visualization for your uncertainty and confidence is an iterative process that involves feedback and evaluation. To ensure your visualization is effective, you should test it with different scenarios, data sets, models, audiences, perspectives, goals, methods, tools, platforms, and more. Evaluate the feedback and make adjustments or modifications as needed. This could include adding or removing elements, changing or enhancing features, or refining or clarifying messages. Doing so will help you create a visualization that communicates effectively to different people in different environments.