In machine learning, the F1 score, also known as the balanced F-score or F-measure, is a metric used to evaluate the performance of binary classification models. It is the harmonic mean of precision and recall, which are two important metrics that assess the ability of a model to correctly identify positive and negative cases. Precision measures the proportion of positive predictions that are actually correct. It is calculated as the number of true positives divided by the total number of positive predictions. Recall measures the proportion of actual positive cases that are correctly identified. It is calculated as the number of true positives divided by the total number of actual positive cases.

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

Recall (R), also known as sensitivity or true positive rate, is the number of true positive predictions divided by the total number of actual positives in the dataset. It measures the ability of the model to capture all the positive instances.

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

The F1 score combines precision and recall into a single metric, balancing their importance and providing a more comprehensive measure of a model's overall performance. It is particularly useful when dealing with imbalanced datasets, where there is a significant difference in the number of positive and negative cases.


The formula for calculating the F1 score is:
F1 = 2 * (precision * recall) / (precision + recall)

A higher F1 score indicates better performance, with a score of 1 representing perfect precision and recall. In general, an F1 score above 0.5 is considered good, and a score above 0.7 is considered excellent.

The F1 score is widely used in various machine learning applications, including: Text classification: Identifying spam emails, sentiment analysis, topic classification. Image classification: Classifying objects in images, detecting skin cancer, identifying traffic signs. Medical diagnosis: Predicting the risk of heart disease, identifying cancer cells, diagnosing Alzheimer's disease. Recommendation systems: Recommending movies, suggesting products, predicting customer churn.

The F1 score is a valuable metric for comparing the performance of different machine learning models and for evaluating the effectiveness of a model on a given task. It is particularly useful in situations where both precision and recall are important, and it can provide a more nuanced assessment of a model's performance compared to using accuracy alone.


The harmonic mean gives more weight to lower values. As a result, the F1 score is high only if both precision and recall are high, making it a useful metric when there is an uneven class distribution or when both false positives and false negatives are important considerations.
In summary, the F1 score is a metric in machine learning that considers both precision and recall, providing a single value that balances the trade-off between these two metrics. It is particularly useful in situations where imbalanced classes or the costs associated with false positives and false negatives need to be taken into account.