The phrase "a range of classifications" is correct and usable in written English.
It can be used when discussing different categories or types within a particular subject or field.
Example: "The study presents a range of classifications for various species of plants based on their growth patterns."
Alternatives: "a variety of classifications" or "a spectrum of classifications".
The prefix and class number provide a range of classifications, from swimmers with severe disability (S1, SB1, SM1) to those with minimal disability (S10, SB9, SM10).
Using 0.05-m and 0.2-m raster grids, we compared a range of classifications, both pixel-based and object-based approaches, including manual, Maximum Likelihood Classifier, Jenks Optimization clustering, textural analysis and Object Based Image Analysis.
In recent years, techniques such as convolutional neural networks have become the dominant technology used by researchers for a range of classification tasks [20].
In this study, we present a framework that can integrate a range of classification algorithms to predict the geographical distribution of a specific event.
A number of feature generation algorithms have been proposed [11], resulting in significant improvements in performance over a range of classification tasks, and further establishing the field in machine learning.
A range of classification methods, such as decision tree, neural network, nearest neighbor, logistic regression, and support vector machine, have been well developed.
First, a range of classification systems was evaluated to assess their suitability as the underlying base for the short lists for the three data elements.
Indeed, diagnostic criteria that were used in these cohort studies were based on a range of classification strategies, including the International Classification of Diseases (ICD), Revisions 7 9, and ICD for Oncology, Revision 3 (ICD-O3) (World Health Organization 1955, 1965, 1975, 2000).
The MicroArray Quality Control (MAQC MAQCQConsortiumum project-II: a comprehensive study of common practices for the development and validation of microarray-based predictive models) breast cancer data set (Table 1) offers a unique opportunity to study the performance of genomic classifiers when applied across a range of classification difficulties.
The reason for choosing this set of bacilli is that these species have previously been analysed using a range of classification approaches including miniaturised biochemical test Analytical Profile Index (API), genotyping using 16S rDNA sequencing and an alternative physciochemical methods to MALDI-MS called Raman spectroscopy that measures molecular vibrations of functional groups.
