How do you improve machine learning accuracy with expert feedback?

2 How to collect expert feedback?

When collecting expert feedback, the goal, budget, and availability of experts should be taken into consideration. Crowdsourcing is a popular method, which involves using online platforms or services to recruit and pay a large number of experts or workers to provide feedback on data or models. Active learning is another option; this machine learning technique allows you to select the most informative or uncertain samples from data or models and ask experts to provide feedback. Interactive learning is a third choice; this technique permits interaction with experts in real time and updates the model based on their feedback. Examples of these methods include Amazon Mechanical Turk or Figure Eight for crowdsourcing, uncertainty sampling or query-by-committee for active learning, and reinforcement learning or online learning for interactive learning.

3 How to use expert feedback?

Using expert feedback, you can improve machine learning accuracy in various ways, depending on your problem, data, and model. For instance, you can use it to remove, correct, or replace erroneous or noisy data that can affect your model's performance. You can also use it to add, generate, or modify data that can enhance your model's performance. Additionally, it can be used to adjust, optimize, or modify your model's parameters, hyperparameters, or architecture. Finally, you can use it to measure, compare, or validate your model's performance by calculating metrics or conducting experiments.

4 What are the benefits of expert feedback?

Expert feedback can provide many benefits for improving machine learning accuracy, such as improving data quality and quantity. This can enhance the model's ability to learn and generalize from the data. Expert feedback can also help tune, optimize, and modify the model, which can make it more robust and reliable. Furthermore, expert feedback can help evaluate, compare, and validate the model, thereby improving its interpretability and explainability. This will enable the model to provide meaningful and understandable results and insights.

5 What are the challenges of expert feedback?

Expert feedback can be a great asset for improving machine learning accuracy, however it can also pose some challenges. Hiring experts can be costly, time-consuming, and difficult to obtain, especially if the domain or problem requires high-level expertise and experience. Additionally, feedback from multiple or diverse sources or methods can be inconsistent, incomplete, or conflicting, so it is important to have a clear strategy for managing and integrating this feedback into your data or model. Moreover, expert feedback can be biased, subjective, or outdated if you rely on feedback from a single source or method. To avoid this issue, you must balance and optimize feedback with other sources of information or learning.

6 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?