Model Performance using F1 Score

Model Performance using F1 Score

In machine learning, evaluating a model’s performance is crucial. One common metric used for classification problems is the F1 Score. It helps measure how well a model balances precision and recall. This is especially useful when dealing with imbalanced datasets where accuracy alone can be misleading.

Precision and Recall

Before understanding the F1 Score, let’s briefly look at two important terms:

• Precision: Out of all the positive predictions made by the model, how many are actually correct?
• Recall: Out of all the actual positive cases, how many did the model correctly predict?

F1 Score Formula

The F1 Score is the harmonic mean of precision and recall. The formula is:

F1 Score = 2 × (Precision × Recall) / (Precision + Recall)

This formula ensures that both precision and recall contribute equally to the final score.

Why Use the F1 Score?

The F1 Score is useful when:

• The dataset is imbalanced (i.e., one class is much more frequent than another).
• Both false positives and false negatives need to be minimized.
• Accuracy alone does not provide enough insight into the model’s performance.

Interpreting the F1 Score

The F1 Score ranges between 0 and 1:

• A score of 1 means perfect precision and recall.
• A score close to 0 means poor performance.

By optimizing for the F1 Score, we ensure a good balance between correctly identifying positive cases and avoiding false alarms.