Best Practices for Evaluating AI Models Accurately
Accurately evaluating the performance of AI models is an important step during development. Model evaluation provides machine learning engineers insights into the strengths and weaknesses of their models. Through the lens of model evaluation metrics, development teams are able to refine and improve their models to meet the desired performance.
When multiple models are in the equation, model evaluation metrics enable the systematic comparison of models and aid in choosing the best one for the use case.
In this article, we’ll discuss the best practices for evaluating models. We’ll focus on:
- Foundational principles of model evaluation
- Choosing suitable metrics for your specific tasks and projects
- Leveraging tools such as FiftyOne to streamline model evaluation
At the end of this article, you’ll walk away with a better understanding of model evaluation best practices and how to use these in your ML work. Whether you’re building models for business applications, safety-critical systems, customer engagement, or more, these insights will empower you to create reliable, high-performing AI systems.
Why Evaluation Matters
AI models have become indispensable across industries such as healthcare, aerospace, retail, finance, and more, solving complex problems and driving data-driven decisions. However, their effectiveness depends entirely on accurate and thorough evaluation. For example:
- Healthcare: A misdiagnosis from a poorly evaluated model could lead to critical health risks.
- Retail: An inaccurate recommendation system may fail to engage users, costing businesses opportunities and revenue.
- Autonomous Vehicles: Safety-critical systems demand rigorous testing to avoid accidents caused by misclassifications.
Risks of Poor AI Model Evaluation
When evaluating AI models, accuracy is paramount. Poor evaluation can lead to costly errors, compromised user safety, and reduced trust in AI systems. Let’s explore the significant risks of inadequate evaluation and why each deserves serious consideration.
- Unaddressed Model Biases: Inadequate testing may allow biases to persist, leading to unfair outcomes and reputation/legal risks.
- Resource Wastage: Rushed evaluations can result in ineffective models, wasting time, money, and effort on solutions that fail
- Safety Risks: Poorly evaluated models can cause safety hazards in critical applications like self-driving cars or medical diagnostics.
- False Confidence in Metrics: Over-reliance on simplified metrics like accuracy can mask real-world performance issues, delaying necessary fixes.
Foundational Principles of Model Evaluation
Building and deploying AI models that perform reliably in real-world settings isn’t easy, but following some core principles can go a long way in making it possible. These principles guide AI teams in designing evaluation processes that lead to dependable, high-quality models ready for real-world applications. Let’s take a closer look at what these principles are and why they matter.
Selecting the Right Evaluation Metrics Beyond Accuracy
Accuracy is a common evaluation metric, but relying on it alone can lead to misleading insights, especially in complex or high-stakes scenarios. For example, in a medical diagnosis task for a rare disease, a machine learning model that predicts “no disease” for every case might achieve 99% accuracy but completely fail to identify actual cases. To evaluate models effectively, it’s essential to consider metrics that reflect their performance more holistically.
Common Evaluation Metrics
Metric | What it Measures | Best Used For |
|---|---|---|
Precision | Proportion of true positives among all positive predictions.
| Tasks where a false positive rate affects costs (e.g., spam detection). |
Recall | Proportion of true positives identified out of all actual positives. | Tasks where false negatives are critical (e.g., medical diagnosis). |
F1 Score | Harmonic mean of precision and recall, balancing the two metrics. | When both precision and recall are equally important (e.g., fraud detection). |
Intersection over Union (IoU)
| Degree of overlap between the predicted and actual values in spatial tasks. | Object detection or segmentation tasks that require precise localization (e.g., self-driving cars).
|
Mean Average Precision (mAP) | How well a model detects objects by considering both Precision and Recall. | Measuring overall performance of detection and segmentation tasks (e.g., facial recognition, visual search). |
Confusion Matrix | Visual representation of predicted classification against known classifications with a breakdown of correct predictions as well as incorrect predictions | To assess the effectiveness of any classification model by identifying where it makes mistakes and which classes are misclassified. |
Domain-Specific Metrics | Custom metrics tailored to specific industries or applications. | Examples include mean absolute error (MAE) for forecasting in finance. |
Measuring Performance for Different Tasks
Using a single performance metric can obscure the model’s true performance. A multi-metric approach, depending on the task, is usually a more pragmatic approach. In this section we’ll talk about the different classification metrics, object detection, and segmentation models.
Classification
The objective of classification models is to find patterns within the data based on finite predefined categories or classes based on features and use that data to predict the class of new, unseen data points. Binary classification refers to predicting two classes, and multi-class classification is involved in predicting, as its name suggests, more than two classes. For example: detecting ‘spam’ or ‘non-spam’ calls is a binary classification, and predicting types of animals or plants in an image is a multi-class system. Identifying objects within an image and assigning them to specific categories falls into image classification.
The typical metrics used for classification models are accuracy, precision, recall, F1 score, and confusion matrix. For example: In fraud detection, combining precision, recall, and F1 score ensures the model is effective at capturing true cases while minimizing false alarms.
Detection and Segmentation
The goal of object detection is to identify and localize objects within an image or a video. Typically algorithms such as YOLO, R-CNN, F-CNN, and SSD are used for detection tasks, and bounding boxes are drawn around the objects. Segmentation, on the other hand, partitions the image into distinct and meaningful regions where each pixel is associated with a class label. Example: In this image, the bounding box identifies the object, and the segmentation identifies the exact region of the object.
The typical metrics used for detection and segmentation models are Intersection over Union (IoU) and Mean Average Precision (mAP)
Data Splitting Strategies for Robust Evaluation
Creating AI models that perform well in real-world settings requires a solid evaluation process, and data splitting is a critical part of it. How you split your data determines how well your model will generalize to new data. Let’s explore the main strategies for effective data splitting and how they can strengthen your evaluation process.
Train-Test Split: The Basic Starting Point
The train-test split is the simplest approach, where you divide your data into two parts: one for training the model and another for testing its performance. A typical split is 80-20 or 70-30, with the larger portion for training.
- Pros: Quick and easy to implement, good for a fast performance snapshot.
- Cons: Sensitive to data variability; may not provide stable results with small or imbalanced datasets.
This method is useful for initial experiments, but for more robust evaluation, advanced techniques are preferred.
Cross-Validation: A More Reliable Alternative
Cross-validation provides a more comprehensive view of the model’s performance by utilizing multiple train-test splits. One popular technique is k-fold cross-validation, where the dataset is divided into k subsets (folds). The model is trained on k−1k-1k−1 folds and tested on the remaining fold, cycling through until every subset has been used for testing once.
- Pros: Reduces bias by using all data for training and testing; provides stable metrics even with limited data.
- Cons: Computationally intensive, especially with large datasets or high k-values.
Here’s an example in Python demonstrating k-fold cross-validation using the scikit-learn library:
#install sklearn by writing pip install scikit-learn
#import necessary packages
from sklearn.model_selection import KFold, cross_val_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
# Load a sample dataset
data = load_iris()
X, y = data.data, data.target
# Initialize the model
model = RandomForestClassifier(random_state=42)
# Set up k-fold cross-validation (k=5)
kfold = KFold(n_splits=5, shuffle=True, random_state=42)
# Evaluate the model
scores = cross_val_score(model, X, y, cv=kfold)
# Display results
print(f"Cross-Validation Scores: {scores}")
print(f"Average Score: {scores.mean():.2f}")
When to Use Cross-Validation
- Small Datasets: Maximizes the utility of limited data.
- Unbalanced Datasets: Ensures all data subsets contribute to evaluation.
- Model Benchmarking: Provides more reliable metrics for comparing different algorithms.
Leveraging FiftyOne for Data Splitting
Splitting data correctly can get complicated, but the FiftyOne tool makes it much easier. FiftyOne offers the ability to manage data splitting, visualize your splits, and even apply custom strategies like stratified sampling. This becomes especially useful if you’re working with large or diverse datasets, where manually handling splits could lead to errors or inconsistencies.
Creating dataset splits (test, train, validation) is a perfect use case for tags in FiftyOne and can be implemented very easily:
import fiftyone as fo
sample = fo.Sample(filepath="/path/to/image.png", tags=["train"])
sample.tags.append("my_favorite_samples")
print(sample.tags)
# ["train", "my_favorite_samples"]
Mitigating Bias and Ensuring Fairness in Evaluation
Bias is a huge challenge in AI, and addressing it is crucial to building models that are fair, reliable, and genuinely beneficial. When bias is left unchecked, models can produce unfair outcomes or skewed results that impact real people, especially in areas like hiring, healthcare, and lending. Tackling bias during model evaluation helps ensure fairness and builds trust, especially in systems that affect diverse communities. Here’s why bias mitigation matters and some practical ways to do it.
Recognizing Bias in Training Data and Test Sets
AI models learn from data, so if there’s bias in the training or test data, the model will likely learn that bias too. For example, if a facial recognition model is trained mostly on images of lighter-skinned faces, it might struggle to accurately recognize darker-skinned faces, leading to biased outcomes. The first step in bias mitigation is recognizing where your data might lack diversity or represent one group more than another. This way, you can better understand where the model might struggle and start planning ways to improve its fairness.
Using Stratified Sampling to Address Imbalances
Imbalanced data is a common issue in AI where certain categories or demographics may dominate the dataset, especially if the data wasn’t collected with fairness in mind. To address this, stratified sampling is a helpful technique. Stratified sampling ensures that each subgroup is properly represented in both the training and testing sets, keeping the dataset balanced.
Testing Across Diverse Demographic Groups
Fairness isn’t achieved if a model performs well overall but fails for specific groups. To address this, it’s essential to evaluate model performance across different demographic groups, such as age, gender, race, and socioeconomic status. By doing this, you can identify if the model has any hidden performance gaps.
Leveraging FiftyOne for Streamlined Model Evaluation
Evaluating an AI model thoroughly can get complex, especially when you’re juggling different metrics, data splits, and analysis tools. FiftyOne steps in as a powerful ally for AI builders, helping simplify and streamline model evaluation. It’s designed to help teams save time, avoid errors, and make more informed decisions, all essential for developing models that truly perform well in real-world settings.
Here’s an example of how to work with aggregate metrics in FiftyOne using the Python SDK:
# Get the 10 most common classes in the dataset
counts = dataset.count_values("ground_truth.detections.label")
classes = sorted(counts, key=counts.get, reverse=True)[:10]
# Print a classification report for the top-10 classes
results.print_report(classes=classes)
precision recall f1-score support
person 0.45 0.74 0.56 783
kite 0.55 0.72 0.62 156
car 0.12 0.54 0.20 61
bird 0.63 0.67 0.65 126
carrot 0.06 0.49 0.11 47
boat 0.05 0.24 0.08 37
surfboard 0.10 0.43 0.17 30
traffic light 0.22 0.54 0.31 24
airplane 0.29 0.67 0.40 24
giraffe 0.26 0.65 0.37 23
micro avg 0.32 0.68 0.44 1311
macro avg 0.27 0.57 0.35 1311
weighted avg 0.42 0.68 0.51 1311
Here’s what it looks like to get a side-by-side comparison of metrics on two models using the Model Evaluation Panel in the FiftyOne Application.
FiftyOne provides a variety of builtin methods for evaluating your model predictions, including regressions, classifications, detections, polygons, instance, and semantic segmentation, on both image and video datasets.
When you evaluate a model in FiftyOne, you get access to the standard aggregate metrics such as mAP, Precision, IOU, classification reports, confusion matrices, and PR curves. In addition, FiftyOne also provides fine-grained statistics like accuracy and false positive counts at the sample level, which you can interactively explore to diagnose the strengths and weaknesses of your models on individual data samples. Analyzing each metric and understanding which samples are causing poor scores and why helps uncover insights and find areas of gaps in your data that are helpful to iteratively improve model performance.
Check out the model evaluation capabilities in FiftyOne and try the Model Evaluation panel that provides an out-of-the-box experience to visualize and interactively explore the evaluation results in the App.
Powerful Visualization Tools for In-Depth Analysis
One of the standout features of FiftyOne is its visualization capabilities, which allow AI teams to dig deep into model performance. With FiftyOne, you can visualize everything from confusion matrices to error distributions, gaining insights into where your model is excelling and struggling.
For example, if a model produces a high rate of false positives, visualization tools can help you pinpoint specific patterns or data segments causing the issue. These insights are crucial for improving model accuracy, reducing bias, and addressing weaknesses that might impact performance.
Use Cases: Evaluating Models for Success
Evaluating AI models isn’t just a theoretical exercise, it has real, tangible consequences ensuring models perform effectively in the field. The next section provides detailed examples of how robust evaluation impacts success, with a brief mention of other potential use cases for those interested in exploring further.
Ensuring Safety in Self-Driving Cars with Object Detection Models
Self-driving cars are a prime example of how AI models must be rigorously evaluated to meet safety and reliability standards. An object detection model in these systems identifies pedestrians, vehicles, traffic signs, and obstacles, playing a critical role in enabling safe navigation.
To ensure reliable performance, developers test the model on diverse datasets representing real-world driving conditions:
- Lighting Variations: Daylight, dusk, and nighttime scenarios.
- Weather Conditions: Rain, fog, snow, or bright sunlight.
- Environments: Urban traffic, rural roads, and highways with varying levels of congestion.
Evaluation practices often involve metrics like Intersection over Union (IoU) for assessing the accuracy of object localization and precision/recall for detecting critical objects (e.g., pedestrians). By exposing the model to such a wide range of scenarios, developers can identify blind spots and improve its robustness.
These rigorous evaluations reduce the risk of accidents, enhance user trust, and accelerate the adoption of autonomous driving technologies. The ability to generalize across diverse conditions is key to achieving reliability and safety in such high-stakes applications.
Broader Applications and Further Reading
Robust evaluation isn’t just vital for self-driving cars, it applies to numerous fields. For instance:
- Healthcare: Evaluating medical image analysis models to ensure accurate and equitable diagnoses.
- Security: Refining facial recognition models to eliminate biases across demographic groups.
To learn more about how evaluation shapes success in AI, explore additional resources on medical and security use cases.
The Road Ahead: Continuous Learning and Improvement
AI models aren’t “one-and-done” solutions; they need ongoing evaluation to stay effective. As the world and data evolve, so must your models. Below are key strategies for ensuring long-term success
Using A/B Testing to Compare Performance
A/B testing is a powerful tool for continuous evaluation. By running two versions of a model and comparing their results, you can see which one performs better in real time. It’s a great way to test new features or changes, allowing you to make data-driven decisions without risking your entire model.
Data Logging and Monitoring
Real-time monitoring is essential for identifying issues as they arise. By implementing data logging and monitoring tools, you can track model predictions and detect any unexpected errors. If something goes wrong, like a sudden drop in accuracy, these tools will alert you right away, helping you address the problem quickly.
Re-Evaluating with New Data
As time goes on, the data you’re using may no longer represent the current landscape. Regularly re-evaluating your model with fresh data helps it stay relevant and accurate. For example, retraining a fraud detection system with new fraud patterns ensures it keeps up with evolving tactics. This ongoing process helps your model adapt and continue performing at its best.
Conclusion
Adopting best practices for AI model evaluation is essential for building reliable, high-performing models. By carefully selecting evaluation metrics, using diverse datasets, and continually assessing model performance, you can ensure that your AI applications succeed. This approach minimizes errors, reduces biases, and boosts trust in your AI systems.
FiftyOne simplifies the evaluation process, providing AI developers with the tools needed to streamline testing, monitor performance, and visualize results. Whether you’re fine-tuning an object detection model or ensuring fairness in a classification system, FiftyOne helps you achieve impactful, reliable results.
Next steps
Voxel51 has made it easy to get started evaluating AI models accurately with FiftyOne.
Looking for a scalable solution for your ML team as you collaborate on visual AI projects? Check out FiftyOne Teams and connect with an expert to see the collaborative features of FiftyOne in action.
