Recall

As explained in Data-Driven Cybersecurity

Recall is the proportion of actual instances in a class that a classification model correctly identifies.

              precision    recall  f1-score   support

           0       0.63      1.00      0.77       126
           1       1.00      0.00      0.00        74

    accuracy                           0.63       200
   macro avg       0.81      0.50      0.39       200
weighted avg       0.77      0.63      0.49       200

How Data-Driven Cybersecurity covers it

How deep it goes

Interprets class-specific recall in classification reports alongside precision, F1-score, accuracy, and confusion matrices.

Examples

Uses cybersecurity articles, network traffic, and login activity to interpret class-specific recall.

What you can do after

Evaluate whether a classifier detects actual threats by reading class-specific recall alongside false negatives and other performance measures.

How it connects

Connects recall with precision, F1-score, accuracy, confusion matrices, and false negatives.

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Also explained in

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  Grokking Machine LearningA classification metric equal to the proportion of positive-labeled data points that are correctly predicted as positive.
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  Starting Data Analytics with Generative AI and PythonThe proportion of actual positive cases correctly classified as positive, equivalent to sensitivity.
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  Natural Language Processing in Action, Second EditionRecall is the proportion of relevant items that a retrieval or classification system successfully identifies.
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  Machine Learning System DesignThe proportion of actual positive cases that a model correctly predicts as positive.
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  Effective Conversational AIRecall is the proportion of relevant or correct cases that a classifier successfully identifies.
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  Delivering Machine Learning Projects: From design to deploymentRecall is the proportion of relevant positive instances that a predictive classification system correctly identifies.
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  Statistics Slam DunkSensitivity is the proportion of actual positive cases that a classification procedure correctly identifies.
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  Knowledge Graphs and LLMs in ActionRecall is the proportion of relevant positive instances that are successfully identified.
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  Deep Learning with PyTorch, Second EditionA classification metric measuring the fraction of actual positives that are correctly identified, which can be undefined when there are no actual positives.
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  Grokking StatisticsTrue positive rate is the proportion of positive instances correctly classified as positive.
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  Statistics Every Programmer NeedsSensitivity is the proportion of actual positive instances that a classifier correctly identifies as positive.
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  Just Use Postgres!Recall is the proportion of relevant items that a search system retrieves from all relevant items available.
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  The Art of AI Product DevelopmentRecall is the proportion of actual positive cases that a classifier correctly identifies.
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  Generative AI in ActionRecall is the proportion of relevant or actual positive cases that a system correctly identifies.
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  Financial AI in PracticeThe proportion of all fraudulent cases found within a selected group of model-ranked cases, such as the top n percent.
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  Ensemble Methods for Machine LearningRecall is the fraction of actual positive examples that are correctly predicted as positive.
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  LLMs in ProductionRecall is the proportion of reference items that are correctly identified by a prediction.
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  Introduction to Generative AI, Second EditionRecall is the proportion of actual positive cases that a classifier correctly identifies.
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  Machine Learning Algorithms in DepthThe proportion of actual positive examples correctly predicted as positive.
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  The AI Pocket BookA classification or retrieval metric measuring how many relevant or positive items are successfully found.
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  Elasticsearch in Action, Second EditionIn information retrieval, recall is the proportion of all relevant documents that are retrieved.
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  Time Series Forecasting Using Foundation ModelsRecall is the proportion of actual positive observations that a detection system correctly identifies.
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  Fabulous Adventures in Data Structures and AlgorithmsTest sensitivity is the conditional probability that a medical test is positive when the tested condition is present.

Compare how 24 books explain recall →

Related concepts

• f-scoreused by · An F-score is a classification or evaluation metric combining precision and recall via their weighted harmonic mean; F1 is the equal-weight case.
• precisioncontrasts with · Precision is the proportion of items identified as positive, selected, or retrieved that satisfy the relevant correctness or relevance criterion.
• false-negative ratecontrasts with · The false-negative rate is the proportion of actual positive cases that are incorrectly classified or identified as negative.
• receiver operating characteristicused by · A receiver operating characteristic curve plots a binary classifier’s true-positive rate against its false-positive rate as the decision threshold varies.
• similarity searchmeasures · Similarity search is a retrieval technique that finds or ranks items by comparing their representations with a query according to a similarity or distance measure.

This explainer was generated by Manning's knowledge graph from the text of Data-Driven Cybersecurity. The section links point to the parts of the book it draws on.