What is f1score?

The F1 score is a single number that tells you how well a classification model is doing at finding the right items while avoiding false alarms. It combines two other measures-precision (how many of the items the model said were positive are actually positive) and recall (how many of the real positive items the model managed to find)-into their harmonic mean. The result ranges from 0 (worst) to 1 (perfect).

Let's break it down

  • Precision = True Positives ÷ (True Positives + False Positives). It answers “When the model predicts positive, how often is it right?”
  • Recall = True Positives ÷ (True Positives + False Negatives). It answers “Out of all real positives, how many did the model catch?”
  • F1 score = 2 × (Precision × Recall) ÷ (Precision + Recall). By using the harmonic mean, the F1 score penalizes extreme imbalances; if either precision or recall is low, the F1 score drops.

Why does it matter?

In many real‑world problems you care about both missing important cases and raising false alarms. Accuracy can be misleading when classes are unbalanced (e.g., 95% of emails are not spam). The F1 score gives a balanced view, helping you pick models that are good at both catching true cases and staying precise.

Where is it used?

  • Spam detection (catch spam without flagging good mail)
  • Medical diagnosis (identify disease cases while limiting false positives)
  • Fraud detection in finance
  • Information retrieval (search engines ranking relevant results)
  • Any binary or multi‑class classification task with uneven class distribution.

Good things about it

  • Works well when classes are imbalanced.
  • Summarizes precision and recall in one easy‑to‑compare number.
  • Simple to calculate and interpret for beginners.
  • Encourages models that balance false positives and false negatives rather than optimizing one at the expense of the other.

Not-so-good things

  • Hides the trade‑off between precision and recall; you don’t see which one is the weaker link.
  • Not suitable when you need to prioritize one metric over the other (e.g., safety‑critical systems may value recall far more).
  • Can be misleading for multi‑class problems if you average incorrectly (macro vs. micro averaging).
  • Doesn’t consider true negatives, so it may not reflect overall performance when those matter.