F1 Score: Balancing Precision and Recall in AI Evaluation

An AI model can have high accuracy and fail when it matters most. The cost of false positives and false negatives can be significant, making precision and recall as important as accuracy.

That’s where the F1 Score comes in—helping balance these trade-offs to ensure models make the right decisions. This article explores what the F1 Score is, its limitations, and how AI teams can move beyond it to build models that perform reliably in real-world scenarios.

What is the F1 Score?

The F1 Score is a metric used to evaluate the balance between precision (how many of the predicted positive cases are actually correct) and recall (how many of the actual positive cases the model successfully identifies). It is particularly useful in scenarios where both false positives and false negatives carry significant consequences. Unlike accuracy, which can be misleading in imbalanced datasets, the F1 Score provides a harmonic mean of precision and recall, ensuring a more reliable assessment of model performance. This makes it an essential metric for applications such as fraud detection, medical diagnosis, and security systems, where misclassification can have serious real-world implications.

Mathematically, the F1 Score is the harmonic mean of precision and recall, calculated as:

The resulting score ranges from 0 to 1, with higher values indicating a better balance between capturing relevant instances (recall) and avoiding incorrect predictions (precision).

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How to Calculate the F1 Score?

Accurately computing the F1 Score is crucial for evaluating AI models, as it provides a balanced measure of both precision and recall.

Unlike the arithmetic mean, the F1 Score relies on the harmonic mean, which prevents extreme values from distorting the evaluation. If either precision or recall is significantly low, the F1 Score remains closer to the lower value, emphasizing the need for both metrics to be high for optimal model performance.

• Calculate Precision
  • Example: If a model identifies 100 fraud cases, but 20 are false positives, precision is: \frac{80}{80 + 20} = 0.80 \text{ (80% Precision)}
• Calculate Recall
  • If there were 120 actual fraud cases, but the model only caught 80, recall is: \frac{80}{80 + 40} = 0.67 \text{ (67% Recall)}
• Compute the F1 Score
  • Plugging precision and recall into the formula:

Thus, the model’s F1 Score is 0.73, meaning it maintains a balanced trade-off between precision and recall.

When to Use the F1 Score?

The F1 Score is most useful in AI evaluation when false positives and false negatives need to be considered equally.

• Imbalanced Datasets: When one class is significantly more frequent than another (e.g., fraud detection, rare disease diagnosis). In scenarios where class distributions are uneven, the F1 Score offers a more reliable alternative to accuracy by balancing precision and recall.
• High-Stakes Decision-Making: In applications where errors have serious consequences (e.g., security threat detection or medical diagnoses), optimizing both precision and recall is essential.
• Trade-offs Between Precision and Recall: If a model needs to strike a balance between catching all relevant cases (high recall) and minimizing false alarms (high precision), the F1 Score serves as an effective evaluation metric.
• Multi-Class Classification: When handling multiple categories, variants such as Macro-F1 and Weighted-F1 provide better insights into model performance across all classes.

What are F1 Score Variants?

While the standard F1 Score is useful for evaluating binary classification models, different variations provide more nuanced insights, particularly in multi-class classification and imbalanced datasets.

Choosing the right variant ensures model performance is measured effectively based on the specific task at hand.

Macro F1 Score – Equal Treatment for All Classes

Macro F1 calculates the F1 Score separately for each class and then takes an unweighted average across all classes. This approach ensures that each class is treated equally, regardless of class distribution, preventing dominant classes from skewing the evaluation. It is particularly useful in scenarios where all classes should contribute equally to model assessment, such as sentiment analysis with multiple categories.

Micro F1 Score – Best for Overall Model Performance

Micro F1 aggregates all true positives, false positives, and false negatives across classes before computing a single F1 Score. Unlike Macro F1, which treats each class separately, Micro F1 evaluates the model as a whole, making it ideal when overall classification performance is more important than per-class evaluation.

This is particularly effective in tasks where class distribution varies significantly, such as fraud detection, where the majority class is overwhelmingly larger than the minority class.

Weighted F1 Score – Handling Class Imbalance

Weighted F1 extends Macro F1 by assigning weights to each class based on its frequency, ensuring that more common classes contribute proportionally to the final score. This is particularly useful in imbalanced datasets, where rare classes may otherwise be underrepresented in model evaluation. In medical AI, for instance, where rare disease cases exist alongside more common conditions, the Weighted F1 Score ensures that model performance is not biased toward the majority class.

Fβ Score – Adjusting the Precision-Recall Balance

The Fβ Score modifies the standard F1 Score by placing more emphasis on either precision or recall, depending on the application’s needs. A lower β value (e.g., F0.5) prioritizes precision, making it useful in applications like spam detection, where reducing false positives is critical. Conversely, a higher β value (e.g., F2) emphasizes recall, which is beneficial in applications like medical screening, where missing a diagnosis is more problematic than generating false positives.

Choosing the Right F1 Score Variant

While the standard F1 Score is useful for evaluating binary classification models, different variations provide more nuanced insights, particularly in multi-class classification and imbalanced datasets.

Choosing the right variant ensures model performance is measured effectively based on the specific task at hand.

Macro F1 Score – Equal Treatment for All Classes

Macro F1 calculates the F1 Score separately for each class and then takes an unweighted average across all classes. This approach ensures that each class is treated equally, regardless of class distribution, preventing dominant classes from skewing the evaluation. It is particularly useful in scenarios where all classes should contribute equally to model assessment, such as sentiment analysis with multiple categories.

Micro F1 Score – Best for Overall Model Performance

Micro F1 aggregates all true positives, false positives, and false negatives across classes before computing a single F1 Score. Unlike Macro F1, which treats each class separately, Micro F1 evaluates the model as a whole, making it ideal when overall classification performance is more important than per-class evaluation.

This is particularly effective in tasks where class distribution varies significantly, such as fraud detection, where the majority class is overwhelmingly larger than the minority class.

Weighted F1 Score – Handling Class Imbalance

Weighted F1 extends Macro F1 by assigning weights to each class based on its frequency, ensuring that more common classes contribute proportionally to the final score. This is particularly useful in imbalanced datasets, where rare classes may otherwise be underrepresented in model evaluation. In medical AI, for instance, where rare disease cases exist alongside more common conditions, the Weighted F1 Score ensures that model performance is not biased toward the majority class.

Fβ Score – Adjusting the Precision-Recall Balance

The Fβ Score modifies the standard F1 Score by placing more emphasis on either precision or recall, depending on the application’s needs. A lower β value (e.g., F0.5) prioritizes precision, making it useful in applications like spam detection, where reducing false positives is critical. Conversely, a higher β value (e.g., F2) emphasizes recall, which is beneficial in applications like medical screening, where missing a diagnosis is more problematic than generating false positives.

Choosing the Right F1 Score Variant

The choice of F1 Score variant depends on the specific requirements of the task. When all classes are equally important, Macro F1 provides a fair evaluation. If overall model performance is the goal, Micro F1 is the preferred option.

Weighted F1 is ideal when some classes appear more frequently than others but still require accurate representation. In cases where either precision or recall carries more significance, the Fβ Score helps adjust the balance accordingly.

Addressing the Limitations of the F1 Score

The F1 Score is a widely used metric for AI model evaluation, but it has key limitations that can lead to misleading assessments in real-world applications.

The F1 Score assumes that precision and recall should always be weighted equally. However, in many real-world applications, one metric is often more important than the other.

• Fraud Detection (High Recall Priority): Missing fraudulent transactions is more costly than flagging a few legitimate ones. A model optimized for high recall ensures fraudulent activities are caught, even at the risk of some false positives.
• Medical Diagnosis (High Precision Priority): Incorrectly diagnosing a healthy patient can lead to unnecessary treatments. High precision is required to minimize false positives, ensuring only truly at-risk patients receive further testing.

Galileo Evaluate enables AI teams to iterate on model performance by running A/B tests, refining precision-recall trade-offs, and improving data quality in real-time to optimize accuracy and reliability.

Evaluating Models on Imbalanced Datasets with Adaptive Metrics

The F1 Score does not account for class distribution, making it unreliable in highly imbalanced datasets. A model may achieve a high F1 Score by performing well on majority-class instances while failing to detect minority-class cases.

• Rare Disease Detection: A diagnostic model may classify healthy patients correctly most of the time but fail to detect rare conditions, skewing the F1 Score.
• Cybersecurity Threats: If a system detects common security events well but misses low-frequency, high-risk attacks, the F1 Score may indicate strong performance while failing in critical areas.

Galileo’s Luna Evaluation Suite addresses these challenges by incorporating research-backed metrics optimized for cost, latency, and accuracy, providing a holistic assessment of AI models in imbalanced datasets.

Extending Beyond Single-Threshold Evaluations to Capture Model Confidence

Since the F1 Score is calculated at a single decision threshold, it does not provide insights into how confident a model is in its predictions. AI applications that rely on probabilistic outputs require a more nuanced evaluation.

• Generative AI and LLMs: AI-generated responses often carry varying levels of confidence, which a static F1 Score cannot capture.
• Risk-Based AI Systems: In financial risk assessment, understanding how confident a model is about classifying a loan applicant as "high risk" is crucial for decision-making.

Galileo’s Agentic Evaluations provide granular error tracking and confidence-based insights, allowing AI teams to assess models across multiple decision thresholds.

Ensuring Contextual Relevance and Fairness Beyond F1 Score Metrics

While the F1 Score measures classification accuracy, it does not account for factors such as bias detection, instruction adherence, and factual accuracy—critical aspects for AI applications in text generation, recommendation systems, and autonomous decision-making.

• Recommendation Systems: AI models must go beyond precision and recall to ensure fair, personalized, and unbiased recommendations.
• LLMs and AI Assistants: Evaluating AI-generated content requires assessing coherence, instruction following, and factual accuracy, which the F1 Score does not measure.

But, you can use Galileo’s Observe module real-time monitoring tools to detect anomalies, measure fairness, and track instruction adherence.

Get Started with Galileo’s AI Evaluation Using the F1 Score

The F1 Score is a key measure of AI model performance, balancing precision and recall to ensure models make the right trade-offs. But real-world AI demands more than a static metric—it requires continuous evaluation and optimization.

Galileo’s Evaluation Intelligence Platform helps teams analyze, test, and refine models with A/B testing, real-time tracking, and deeper performance insights. Whether improving model decisions or fine-tuning thresholds, Galileo ensures AI systems are built for accuracy and reliability where it matters most.

Learn more about Galileo AI