Precision Recall Analysis
Precision Recall Analysis - Related Videos
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Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm
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The elicited imitation procedure was established to examine the development of recall memory in infancy and early childhood. This procedure has been widely used to establish a solid foundation of the nature of recall memory in infancy and early childhood.
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Quantitative evaluation of bacterial growth is essential to understanding microbial physiology as a systems-level phenomenon. A protocol for experimental manipulation and an analytical approach are introduced, allowing for precise, high-throughput analysis of bacterial growth, which is a key subject of interest in systems biology.
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Precise and High-Throughput Analysis of Bacterial Growth Using a Microplate Reader
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Source: Kurokawa, M., et al. Precise, High-throughput Analysis of Bacterial Growth. J. Vis. Exp. (2017)This video demonstrates the high-throughput quantification of bacterial growth using a microplate reader. It outlines the steps involved in culture preparation, optical density measurement, and analysis of growth dynamics.
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Uncertainty in Measurement: Accuracy and Precision
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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. Suppose a quality control...
07:05
The Precision of Visual Working Memory with Delayed Estimation
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Source: Laboratory of Jonathan Flombaum—Johns Hopkins University Human memory is limited. Throughout most of its history, experimental psychology has focused on investigating the discrete, quantitative limits of memory—how many individual pieces of information a person can remember. Recently, experimental psychologists have also become interested in more qualitative limits—how precisely is information stored? The concept of memory precision can be both intuitive and elusive at once. It is...
FAQs
Changing the decision threshold changes which cases the classifier labels positive, so precision and recall move in a meaningful tradeoff. A more permissive threshold may capture more true positives but also admit more false positives, whereas a stricter threshold can reduce incorrect positive calls while missing additional positives. Examining the full curve prevents performance from being judged at one arbitrary cutoff.
Precision and recall represent different error priorities. High precision indicates that predicted positives are usually correct, while high recall indicates that the classifier detects a large share of the true positives. In genomic interpretation, emphasizing one over the other depends on whether incorrect positive calls or missed variants create the greater concern, so a single score can conceal an important operational choice.
Average precision provides a summary for comparing models across precision-recall results rather than relying only on one selected threshold. This is particularly useful when positive examples are uncommon, because the analysis focuses on how correctly predicted positives are recovered as recall changes. Researchers can use the measure to compare variant-calling or prediction pipelines before deciding which operating point fits their purpose.
When relevant cases are uncommon, a classifier can produce many negative results while the positive subset remains the critical evaluation target. Precision-recall analysis keeps attention on correct positive calls and detected true positives, alongside false-positive and false-negative outcomes. That focus helps researchers evaluate performance in settings where genomic events of interest represent only a small portion of all cases.
To perform the analysis, collect classifier outputs and known case labels, then evaluate the outputs at multiple decision thresholds. At each threshold, count true positives, false positives, and false negatives; calculate precision and recall; and plot the paired values as a precision-recall curve. The resulting curve shows how performance changes as the classification rule becomes more or less selective.
Threshold selection should follow the intended genomic interpretation task rather than a universal default. Inspect the curve and identify the point that provides an acceptable balance between correct positive calls and detected positives, then consider whether the application is more sensitive to false positives or false negatives. This approach supports reliable interpretation by linking the classifier setting to the task’s error priorities.
In genetics, the same evaluation framework can be applied to variant-calling pipelines, pathogenicity predictors, and disease-risk classifiers. For a variant caller, it can reveal the tradeoff between recovering true variants and introducing incorrect calls; for predictors and risk classifiers, it helps assess how reliably positive predictions correspond to relevant cases. Comparing these results supports more careful genomic interpretation.