Examples of 'precision and recall' in a sentence
Meaning of "precision and recall"
precision and recall: In the context of information retrieval or data analysis, precision and recall are two key metrics used to evaluate the performance of a search algorithm or model. Precision measures the accuracy of the retrieved results, while recall measures the completeness of the results
How to use "precision and recall" in a sentence
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precision and recall
Results are provided in terms of precision and recall.
Precision and recall are better measures in such cases.
This is usually measured by precision and recall.
But a precision and recall evaluation is still valid.
Another interpretation for precision and recall is as follows.
Both precision and recall are therefore based on an understanding and measure of relevance.
And then compute the precision and recall.
Their results show significant improvements in terms of the effectiveness measured through precision and recall.
This is because precision and recall are inversely.
Keyphrase extractors are generally evaluated using precision and recall.
This results in high precision and recall and a smaller search for the user.
Commonly used metrics include the notions of precision and recall.
Precision and Recall.
Illustration of precision and recall.
We also present the possibility to let users choose a preference between precision and recall.
See also
Usually, precision and recall scores are not discussed in isolation.
It is thanks to you that dCode has the best Precision and Recall tool.
Metrics derived from accuracy, precision and recall were proposed and used to evaluate this method.
Overall, it presents the best results both on precision and recall.
O Precision and recall against online gold standard,.
Both RF and SVM generated equivalent precision and recall values.
The plots for precision and recall are as shown in Figure 4.
In doing so we leveraged client2vec 's ability to achieve a consistent balance between precision and recall.
So, we can calculate the precision and recall of each class.
Confusion matrices contain sufficient information to calculate a variety of performance metrics, including precision and recall.
Precision and recall exhibit a trade-off relationship; when one increases, the other decreases.
The quality of a search is typically described using precision and recall metrics,.
Let us calculate precision and recall based on the results shown in Figure 1,.
The quality of the classication results was measured using accuracy, precision and recall measurements.
It 's a [ INAUDIBLE ] mean of precision and recall as defined on this slide.
The F1 score is basically the harmonic mean between precision and recall.
The F-measure combines precision and recall in a single value,.
The F1 value is a measure of the balance between Precision and Recall.
A measure of 1 for F1 means perfect Precision and Recall ( which is very rare ).
Traditional evaluation metrics, designed for Boolean retrieval or top-k retrieval, include precision and recall.
The F1 score is the harmonic mean of precision and recall.
Systems like DSSim, X-SOM or COMA++ obtained at the moment very high precision and recall.
The F1 measurement is the harmonic mean of the precision and recall rates.
F1-Score is the weighted average of Precision and Recall.
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Examples of using Precision
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