Recall

Recall is a metric used to evaluate classification models. The recall of a model is the proportion of positive examples the model classified correctly out of all the positive examples.

$$\text{recall}=\frac{\text{number of correct positive predictions}}{\text{total number of positive examples}}$$

In other words, recall asks, “What proportion of positive examples did the model classify as positive?”

Recall ranges from 0 (the worst performance) to 1 (the best performance).

Computing recall in scikit-learn

import numpy as np
from sklearn.metrics import recall_score

y_true = np.array([1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1])
y_pred = np.array([1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1])
recall = recall_score(y_true, y_pred)

print(f"Recall: {recall:.2f}")

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This page references the following sources:

• Google Developers Machine Learning Crash Course
• scikit-learn API reference