Beyond Accuracy: Evaluating AI with Precision and Recall, AI Short Lesson #31

AI pioneer Andrew Ng is starting a new fund to boost AI innovation. He stresses the importance of using metrics like precision and recall. These help make sure AI models work well, focusing on ai evaluation and precision and recall metrics¹.

As more money goes into AI research, it’s key to check AI models beyond just how accurate they are. Precision and recall metrics give a deeper look at how AI performs. This is vital for evaluating AI².

Key Takeaways

• Evaluating AI models beyond accuracy is essential for optimal performance.
• Precision and recall metrics provide a more complete view of AI performance.
• AI evaluation metrics, such as precision and recall, are key in finance, healthcare, and marketing².
• Good evaluation metrics can spot problems like language model hallucinations and boost AI performance¹.
• Precision and recall metrics help balance false positives and negatives in AI².
• TinyML technology makes it easier for students to learn ML with little resources, covering the whole ML process³.

Understanding the Limitations of Accuracy in AI Evaluation

When we check how well machine learning models work, we must look beyond just accuracy. We need to use many different ways to measure their performance. This is because, as Paul Smolensky and R. Thomas McCoy point out, AI systems need a deeper understanding of their own strengths and weaknesses⁴.

Accuracy can be misleading. Even if a system seems very accurate, it might not be perfect. For example, an HIV test might show 99.3% accuracy, but this could be wrong if the disease is rare⁴. We must look at other important factors like how well we can understand the model’s decisions and how it might be biased.

In medical AI, high accuracy numbers need careful checking. They might be too good to be true, due to problems with the data or how the model was tested⁵. It’s also key to use real data and check how well the model does against it. The quality of the data used to train AI is very important, and small mistakes can add up⁴.

To really get what’s going on with AI, we should look at other metrics too. For instance, precision and recall give us a clearer picture of how well a model does. A model might be very precise but miss a lot of cases⁶. By using these metrics, we can make AI models better and more reliable.

For more details on how to evaluate machine learning models, check out this resource. It explains why precision and recall are so important in judging AI.

Beyond Accuracy: Evaluating AI with Precision and Recall

Evaluating AI models is more than just checking their accuracy. It’s about looking at precision and recall metrics too. Katja Grace, founder of AI Impacts, says it’s key to understand AI’s timeline and impact. Metrics like precision and recall give us valuable insights⁷.

Precision shows how many true positives are among all predicted positives. Recall shows how many true positives are among all actual positives⁸.

It’s important to look at both precision and recall when checking AI models. They often go hand in hand, but improving one might lower the other⁸. The F1-score, which combines both, helps balance them out⁸. Also, ai assessment methods like ROC-AUC and confusion matrices offer more insights into how well a model performs.

Some key things to think about when checking AI models include:

• Precision and recall metrics
• F1-score and its implications
• ROC-AUC and its applications
• Confusion matrices and their role in model evaluation

By looking at these factors and using good ai assessment methods, we can understand AI models better. This helps us make better choices⁷. As we keep improving AI, focusing on precision and recall, along with other metrics, is key. This ensures our models are accurate, reliable, and work well⁸.

Mastering Precision Metrics in AI Assessment

Precision metrics are key in checking how well AI models work. They help spot true positives from false ones. When looking at ai performance, precision vs recall is very important. It greatly affects how well a model does its job⁹.

A model with high precision but low recall might be too careful. On the other hand, a model with high recall but low precision might find too many false positives.

To figure out precision scores, developers use a simple formula: precision = true positives / (true positives + false positives)⁹. This is vital in areas where mistakes can have big effects, like in medicine or finance. Sadly, 75% of businesses see AI model performance drop without regular checks¹⁰.

In real life, precision helps check how AI models do in different areas. For example, in spam email detection, finding the right balance between precision and recall is key⁹. By focusing on precision, developers can make AI models more accurate and trustworthy. This is very important in places where getting things right is essential¹¹.

Understanding Recall in Machine Learning Models

Recall is a key metric in machine learning. It shows how well a model finds all relevant cases in a dataset. It’s calculated as: Recall = True Positives / (True Positives + False Negatives)¹². This is vital in areas like medical diagnoses or fraud detection, where missing a case can be serious.

In these fields, a high recall score is critical. It ensures most actual positive cases are correctly predicted. For example, in analyzing 60,000 emails, the recall score was 0.96 (96% correct)¹³.

In evaluating machine learning models, recall is paired with precision. The F1 Score, which balances both, is a useful metric. It’s calculated as: F1 = 2 * (Precision * Recall) / (Precision + Recall)¹². This helps developers understand their model’s strengths and weaknesses.

The role of recall in ai evaluation is huge. It offers a deeper look at a model’s performance than accuracy alone. In imbalanced classification, a model might show high accuracy but miss the minority class. This shows the need for metrics like recall for a full picture¹².

By focusing on recall, developers can build more reliable models. These models meet the needs of their applications better.

• Recall and precision have a trade-off; increasing one often means decreasing the other¹³
• Choose the right metrics for your application, like precision for medical or recall for fraud detection¹³
• Use techniques like thresholding to improve performance and balance precision and recall¹²

Conclusion: Implementing Effective AI Evaluation Strategies

When checking AI models, it’s key to look at more than just how accurate they are. We should also check their precision and recall. This makes sure the models work well and get the right results.

In medical systems, for example, it’s important to catch all cases to avoid missing diagnoses¹⁴. But in finance, it’s better to be sure about who’s a fraud to avoid false alarms¹⁴.

To make AI evaluation work, use tools like FiftyOne for splitting data. Also, use ai assessment methods that focus on precision and recall. This way, developers can make AI that’s both reliable and fast.

Using methods like cross-validation and stratified sampling helps too. They stop models from fitting too closely to the data and ensures they work well with new information¹⁵.

By taking a detailed approach to AI evaluation, developers can make the most of their models. This is true in fields like healthcare, finance, and self-driving cars. By focusing on beyond accuracy: evaluating ai with precision and recall, developers can make models that are not just right but also dependable and quick¹⁵.

Source Links

1. Episode #31 – AI Weekly: by Aruna – https://www.linkedin.com/pulse/episode-31-ai-weekly-aruna-aruna-pattam-lvqgc
2. learning – https://github.com/Ishannaik/learningml
3. Widening Access to Applied Machine Learning With TinyML – https://hdsr.mitpress.mit.edu/pub/0gbwdele
4. Decoding AI Accuracy: Unraveling Biases, Complexities, and Limitations • DigitalOwl Blog – https://www.digitalowl.com/blog/decoding-ai-accuracy-unraveling-biases-complexities-and-limitations
5. A practical guide to the implementation of AI in orthopaedic research, Part 6: How to evaluate the performance of AI research? – https://pmc.ncbi.nlm.nih.gov/articles/PMC11141501/
6. Beyond Accuracy: Recall, Precision, F1-Score, ROC-AUC – https://medium.com/@priyankads/beyond-accuracy-recall-precision-f1-score-roc-auc-6ef2ce097966
7. Microsoft Word – Measuring AI Systems Beyond Accuracy Final Draft.docx – https://arxiv.org/pdf/2204.04211
8. How can you evaluate Machine Learning models beyond accuracy? – https://www.linkedin.com/advice/0/how-can-you-evaluate-machine-learning-models-beyond
9. Mastering AI Metrics: Beginner’s Guide to Accuracy, Precision, and Recall – https://medium.com/@jdseo/mastering-ai-metrics-beginners-guide-to-accuracy-precision-and-recall-663965b7d26
10. Mastering LLM Evaluation: Metrics, Frameworks, and Techniques – Galileo AI – https://www.galileo.ai/blog/mastering-llm-evaluation-metrics-frameworks-and-techniques
11. AI Metrics Mastery – Measuring Success Beyond Clicks – https://www.linkedin.com/pulse/ai-metrics-mastery-measuring-success-beyond-clicks-felipe-negron-ji9jc
12. Precision and Recall in Classification Models | Built In – https://builtin.com/data-science/precision-and-recall
13. Beyond Accuracy: Understanding Precision and Recall in Machine Learning – https://benjohnokezie.medium.com/beyond-accuracy-understanding-precision-and-recall-in-machine-learning-9a07db9bc46c
14. Precision and Recall in Machine Learning – https://www.analyticsvidhya.com/articles/precision-and-recall-in-machine-learning/
15. Training and Evaluation of AI/ML Models – https://open.ocolearnok.org/aibusinessapplications/chapter/chapter-4-training-and-evaluation-of-ai-ml-models/