Precision vs Recall: What to Use and When

Interactive Guide: Precision vs Recall vs F1 Score

Precision and Recall are essential metrics for evaluating classification models. This interactive guide explains how they work and lets you experiment with them.

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Confusion Matrix Overview

Most classification metrics are derived from a confusion matrix.

True Positive (TP)
Correct positive prediction

False Positive (FP)
Incorrect positive prediction

False Negative (FN)
Missed positive case

True Negative (TN)
Correct negative prediction

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1. Precision

Precision measures the proportion of correctly predicted positive instances out of all predicted positives.

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

🔍 When to Use Precision

• False positives are costly
• You want high confidence in positive predictions
• Examples: spam filtering, medical diagnoses

$ evaluate_model --metric precision

True Positives: 80
False Positives: 20

Precision = 80 / (80 + 20)
Precision = 0.80

💡 High precision means very few incorrect positive predictions.

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2. Recall

Recall measures how many actual positive cases were successfully identified.

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

🔍 When to Use Recall

• False negatives are costly
• You want to detect as many positives as possible
• Examples: cancer detection, fraud detection

$ evaluate_model --metric recall

True Positives: 80
False Negatives: 10

Recall = 80 / (80 + 10)
Recall = 0.89

💡 High recall means very few real positives are missed.

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3. F1 Score (Balance between Precision and Recall)

F1 Score is the harmonic mean of precision and recall. It balances both metrics into one number.

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

🔍 Why F1 Score Matters

• Useful when classes are imbalanced
• Balances false positives and false negatives
• Common in machine learning competitions

$ evaluate_model --metric f1

Precision: 0.80
Recall: 0.89

F1 Score = 2 * (0.80 * 0.89) / (0.80 + 0.89)
F1 Score = 0.84

💡 F1 Score gives a balanced evaluation when both precision and recall matter.

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Interactive Metric Simulator

Adjust the sliders below to simulate model predictions and see how the metrics change.

True Positives:

False Positives:

False Negatives:

Live Metrics

Precision:

Recall:

F1 Score:

💡 Try increasing False Positives to see precision drop. 💡 Increase False Negatives to see recall drop.

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Summary

• Precision measures prediction accuracy for positives.
• Recall measures how many real positives are detected.
• F1 Score balances both metrics.

💡 The best metric depends on the real-world cost of false positives and false negatives.