Recall
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What is Recall?
Recall measures how well a model identifies all the actual positive cases. It answers the question: "Of all the actual positive instances, how many did the model correctly predict as positive?". A high recall means the model is good at minimizing false negatives. The formula for recall is: Recall = True Positives / (True Positives + False Negatives).
Free resources to learn Recall
- ArticleRecall | scikit-learn
More in stage 26: Metrics to Evaluate
Recall: common questions
Can I learn Recall in Hindi?
This lesson is written in English. Once you sign in, open it in the interactive course and ask the AI tutor to explain Recall in Hindi or Hinglish. The tutor answers from this lesson’s own text, so it stays on the syllabus, and it runs on your own Google, OpenAI or Anthropic key.
Is this Recall tutorial for beginners?
It is lesson 101 of 150 in the free Machine Learning roadmap, in stage 26, Metrics to Evaluate. If you are new to Machine Learning, start from stage 1 and come back here in order; if you already know the earlier stages, this lesson stands on its own.