What is recall?

Recall measures the fraction of actual positive cases that a classifier correctly identifies as positive. It answers: *“Of all cases that truly matter, how many did the model find?”*

Formula

Recall = TP / (TP + FN)

• TP (true positives): actual positives correctly detected.
• FN (false negatives): actual positives the model missed.
• TP + FN: the total number of actual positives, regardless of prediction.

Recall is also called sensitivity or true positive rate (TPR). For a fixed binary classification threshold, it ranges from 0 to 1 (0%–100%).

Worked example: screening for defective parts

A factory actually produces 50 defective parts. Its inspection model:

• Flags 45 defective parts correctly (TP = 45).
• Misses 5 defective parts (FN = 5).

Recall = 45 / (45 + 5)
       = 45 / 50
       = 0.90 = 90%

Interpretation: the model detects 9 out of every 10 defective parts in this sample. The other 10% slip through. The number of non-defective parts incorrectly flagged (false positives) does not appear in the recall formula.

Why is recall important?

Recall matters especially when missing a real positive case is costly:

• Disease screening: missing a condition can delay follow-up care.
• Fraud detection: missed fraud can cause financial loss.
• Equipment failure detection: missed warning signs may lead to downtime.

High recall does not mean positive predictions are usually correct: a classifier can label nearly everything positive, catching all real positives while also generating many false alarms. Precision describes that other aspect.

Python example

PYTHON
from sklearn.metrics import recall_score, confusion_matrix

# Positive class = 1 (defective)
# TP=45, FN=5, FP=10, TN=40
y_true = [1]*45 + [1]*5 + [0]*10 + [0]*40
y_pred = [1]*45 + [0]*5 + [1]*10 + [0]*40

print(recall_score(y_true, y_pred, pos_label=1))  # 0.9
print(confusion_matrix(y_true, y_pred, labels=[0, 1]))
# [[40 10]
#  [ 5 45]]

Common pitfalls

• Recall is not precision: recall divides by actual positives (TP + FN), while precision divides by predicted positives (TP + FP).
• Threshold choice matters: reducing the threshold often raises recall but can create additional false positives; the precise trade-off depends on the model and data.
• Define the positive class: when labels differ from 0/1, specify pos_label. For multiclass evaluation, choose an appropriate classwise or averaging scheme.
• No actual positives: when TP + FN = 0, recall is undefined mathematically. In scikit-learn, zero_division controls its handling.

In short: recall measures how successfully a classifier finds actual positives. If missed cases are especially dangerous or expensive, recall is a key metric—but should not be considered in isolation.

References

• scikit-learn: recall_score
• Google Machine Learning Crash Course: recall