Recall is an evaluation metric used in classification. It measures how many actual positives were found by the model.
Recall = True Positives / All Actual Positives
Out of all examples that were truly positive, how many did the model catch?
Recall focuses on the model's ability to find positive cases.
Recall matters when missing a positive case is costly or dangerous. A false negative happens when the model predicts negative, but the true answer is positive.
High recall is important because the model should find as many true cases as possible.
Actual positives are all examples that truly belong to the positive class. They include true positives and false negatives.
A true positive happens when the model predicts positive and the true label is also positive. These are positive cases the model successfully found.
A false negative happens when the model predicts negative, but the true label is positive. These are actual positive cases the model missed.
Recall is calculated using true positives and false negatives.
Recall = True Positives / (True Positives + False Negatives)
Short Form: Recall = TP / (TP + FN)
The denominator means: All actual positives.
Imagine a model is trying to identify positive cases in a dataset.
The model found 70% of the actual positive cases.
High recall means the model finds most actual positives. It is useful when missing positives is dangerous.
Low recall means the model misses many actual positives. It can be risky when the positive class is important.
In medical diagnosis, recall is often very important because missed cases can delay care.
A high recall means the model finds most patients who may need care.
In fraud detection, recall measures how many actual fraud cases were found. Higher recall helps reduce missed fraud.
Recall is important in safety systems because missing a dangerous event can be harmful.
In spam detection, recall measures how many actual spam emails were caught. High recall means most spam emails are caught, but precision should also be checked.
Accuracy measures overall correctness. Recall focuses only on actual positives.
Out of all predictions, how many were correct?
Out of all actual positives, how many did the model find?
A model can have high accuracy but low recall when positives are rare.
Recall and precision measure different parts of performance. Recall focuses on avoiding false negatives, while precision focuses on avoiding false positives.
Out of all actual positives, how many were found?
Disease: How many sick patients were found?
Out of all predicted positives, how many were actually positive?
Disease: How many predicted sick patients were actually sick?
Recall and precision often have a tradeoff. Increasing recall can lower precision, and increasing precision can lower recall.
Recall is more important when false negatives are costly.
Main goal: Find as many actual positives as possible.
Precision may be more important when false positives are more costly.
Main goal: Positive predictions should be trustworthy.
A confusion matrix shows true positives, false positives, true negatives, and false negatives. Recall uses true positives and false negatives.
Recall does not directly use True Negatives or False Positives.
Formula: Recall = TP / (TP + FN)
This example shows how recall is calculated from a confusion matrix.
The model found 82% of the actual disease cases.
Perfect recall means the model found every actual positive. However, it does not always mean the model is perfect.
The model missed no positive cases, but may still make many false positive predictions.
Recall alone does not tell the full story.
A model predicts every transaction as fraud.
Problem: The model may also incorrectly flag thousands of normal transactions. This is why recall should often be considered with precision.
F1 score combines precision and recall into one balanced metric.
Recall is often important in imbalanced datasets, especially when the positive class is rare but important.
Most transactions are normal. Accuracy can be high while missing most fraud.
Recall helps answer: Out of all fraud cases, how many did the model catch?
Improving recall usually means helping the model find more actual positives.
Many classification models output probabilities. A threshold decides when the model predicts positive.
A lower threshold makes the model flag more cases as positive, which may catch more actual positives.
Lowering the threshold can improve recall, but it may reduce precision.
A disease screening model should often have high recall because missing a real disease case may be harmful.
A fraud detection model with low recall may miss many fraudulent transactions.
Recall can measure how many relevant documents were found. High recall is useful when missing relevant information is costly.
The system found 75% of relevant documents.
A disease detection model is tested on 120 patients who actually have a disease. The model correctly identifies 90 of them and misses 30.
Calculate the recall. Then explain what the recall score means and why false negatives matter in this example.
Ask the AI if you need help understanding recall, actual positives, true positives, false negatives, confusion matrices, thresholds, or the difference between recall and precision.