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Dispelling the Myth: Is F1-Score Actually Better Than Classification Accuracy?

By Danish Hasarat posted Thu March 28, 2024 02:38 PM


In machine learning, where models sort things into categories, choosing the right way to measure their success is important. Accuracy, which is how often the model guesses correctly, is a popular choice. But there’s another option: F1-score.

F1-score considers both how good the model is at finding the right things (like catching spam emails) and how good it is at avoiding mistakes (like not labeling a real email as spam). This blog post will explore the pros and cons of accuracy and F1-score, and help you decide which one is better for your situation.

Understanding Accuracy

Accuracy, perhaps the most intuitive metric, calculates the ratio of correct predictions to the total number of predictions made by the model. Mathematically, it is represented as:

Accuracy = Number of Correct Predictions / Total Number of Predictions

Consider a binary classification problem where a model predicts whether an email is spam (positive class) or not (negative class). If the model correctly identifies 90 out of 100 emails as spam, its accuracy would be 90%.

The Drawbacks of Accuracy

Accuracy sounds great at first — it just tells you how often the model gets things right. But there’s a catch. Imagine a system sorting emails into spam and not spam. Spam emails might be rare, so a model that just labels everything “not spam” would have very high accuracy! But that wouldn’t be very helpful, right? This is a problem called the “accuracy paradox” — accuracy can be misleading when there are way more of one type of thing than another. That’s why accuracy might not always be the best way to judge how well a model is doing.

Introducing Precision, Recall, and F1-Score

To overcome the limitations of accuracy, we can use two other metrics: precision and recall.

Precision focuses on how good the model is at picking the right things. In our spam email example, precision would tell us what percentage of emails the model labeled as spam were actually spam.

Precision = True Positives / (True Positives + False Positives)

A high precision indicates that the model has a low false positive rate, meaning it correctly identifies most positive instances without misclassifying negative instances as positive.

Recall, also known as sensitivity or true positive rate, measures the ability of the model to correctly identify all positive instances. It answers the question: “Of all actual positive instances, how many did the model correctly predict as positive?”

Recall = True Positives / (True Positives + False Negatives)

F1-score combines precision and recall into a single metric, providing a balanced measure of a classifier’s performance. It is particularly useful in scenarios where achieving both high precision and high recall is desirable.

F1-score = 2× (Precision x Recall)/(Precision + Recall)

Choosing the Right Metric

The decision between accuracy and F1-score is determined by a variety of criteria, including dataset features and task constraints. Here is a comparison.

Accuracy is simple to understand and explain, making it ideal for situations in which all classes are equally significant and balanced.
F1-score gives a more comprehensive evaluation, especially in imbalanced datasets, by taking into account both false positives and false negatives.

In scenarios where false positives and false negatives have different implications (e.g., medical diagnosis), F1-score may be preferred over accuracy.


While classification accuracy and F1-score are important metrics for evaluating classification algorithms, their usefulness varies according to the context.

Accuracy is a straightforward measure of correctness, whereas F1-score provides a more nuanced evaluation, particularly in skewed datasets. Understanding each metric’s strengths and limits is critical for accurately measuring model performance and making educated decisions in machine learning jobs.

By defining the differences between accuracy and F1-score, we may remove the illusion that one metric is always superior to the other. Instead, we may recognise their complementary roles in evaluating classification models and select the best metric based on the job at hand.