The phrase "algorithm quality" is correct and usable in written English.
It can be used when discussing the effectiveness, efficiency, or performance of an algorithm in various contexts, such as computer science, data analysis, or software development.
Example: "The algorithm quality significantly impacts the overall performance of the application, leading to faster processing times and better user experience."
Alternatives: "algorithm performance" or "algorithm effectiveness".
While Discover Weekly purposefully showed you artists you hadn't heard of, Spotify's algorithm quality is much easier to judge based on Release Radar.
Using the average and the standard deviation data in Table 4, the effectiveness of the proposed algorithm (quality of solutions) is tested.
"I would say the algorithms are as good as they can be with the current data sets, but mobile and social are going to bring new data sets, and you'll start to see step-change functions in algorithm quality," promises Yagan.
Only conserved targets were utilised and each predicted target assigned a rank according to the TargetScan algorithm quality measure known as the total context+ score.
Emotions can overshadow clinical algorithms, quality control measures, even medical experience.
The conference addressed test performance data, the implementation of new testing algorithms, quality assurance, and the application of new tests in a variety of settings.
The time complexity analysis of our new CASE algorithms, quality of the search results, and running time performance compared to those of the traditional methods for the four cases were also discussed in the last sections.
The algorithms' quality was systematically validated versus epitope databases and was found to induce low FP and FN error rates.
Several bioinformatics programs (pipelines) have been developed for automatic SNP mining, using different input data, computational algorithms, quality evaluation strategies, and/or output formats.
Genotyping platforms, calling algorithms, quality control before imputation, imputation methods and analysis software used were all study-specific (see supplementary tables S4 and S5).
To this end, efforts were made to retain the capacity to derive or extend existing outcome measures (e.g., scales related to cognition, ADL, pain, depression, behaviour) and decision support algorithms (e.g., case mix algorithms, quality indicators).
