# F1 Score

## What is F1 Score?

F1 Score is a common evaluation metric used in classification tasks to measure the model's accuracy. It considers both precision and recall, offering a balanced perspective on model performance.

## How F1 Score Works

F1 Score is the harmonic mean of precision and recall. Precision is the ratio of true positives to the sum of true positives and false positives, while recall is the ratio of true positives to the sum of true positives and false negatives.

The F1 Score formula is as follows:

F1 Score = 2 * (precision * recall) / (precision + recall)

## Why F1 Score is Important

F1 Score provides a single metric that combines precision and recall, allowing for a holistic evaluation of a classification model's performance.

By considering both precision and recall, F1 Score helps to identify situations where a model can correctly identify positive instances (precision) while also being able to capture all positive instances (recall).

Maximizing F1 Score indicates finding the right balance between precision and recall for a specific classification problem. It is especially useful when there is an imbalance in the class distribution.

## The Most Important F1 Score Use Cases

• Medical Diagnosis: F1 Score is crucial in medical diagnosis to balance the trade-off between correctly diagnosing positive cases and reducing false positives.
• Spam Filtering: F1 Score helps measure the effectiveness of spam filters in correctly classifying spam emails while minimizing false positives.
• Quality Control: F1 Score is used to evaluate the performance of quality control models in identifying defective products while minimizing false negatives.

Some related terms include:

• Precision: Precision measures the model's ability to identify true positives correctly.
• Recall: Recall measures the model's ability to capture all positive instances.
• Accuracy: Accuracy measures the overall correctness of the model's predictions.
• Confusion Matrix: A confusion matrix provides a tabular representation of the model's performance by comparing predicted and actual class labels.
• AUC-ROC: AUC-ROC (Area Under the Receiver Operating Characteristic curve) is another evaluation metric that measures the model's performance in binary classification tasks.

## Why Dremio Users Would Be Interested in F1 Score

Dremio users, especially those involved in data processing and analytics, can benefit from understanding F1 Score. By evaluating the model's performance using F1 Score, Dremio users can effectively assess the accuracy of their classification models.

Being able to measure the balance between precision and recall helps users make informed decisions about model improvements and optimize their data processing pipelines for classification tasks.