Exact(1)
To solve this problem, we describe the definition of each type of defect (as defined in Table 1) so practitioners can understand the classification of defects that the technique helps to identify.
Similar(59)
These students may have given an otherwise reasonable explanation, but their classification of an amino functional group as a strong base led to their responses being rated as level 2. Still, these students demonstrate some understanding of acid base behavior, but do not appear to understand the strong/weak classification.
This entity has recently been reclassified to reflect increased understanding of the underlying pathology and thus it is crucial for radiologists to understand the new classification, the role of radiology in identifying pre-invasive lesions and the guidelines for management of subsolid nodules.
To better understand the binary classification problem, let Ω be the population of a training dataset, X 1,X 2,…,X q be the set of q selected KPIs and C be the target attribute that indicates the root cause and takes only a finite number of different values, i.e., C={c 1,c 2,…,c q+1}, where c 1 represents the normal behavior and q is the number of potential faults.
We want to make clear that we understand the term classification in a methodical way.
Using CARDIA data, we compared the validity of six approaches for classifying perimenopause status in order to better understand the performance of classification techniques which can be applied to general cohort data.
To understand the differences in classification between the methods, MEGABLAST was used on all sequences uniquely classified by one method.
To understand the computer's classification decisions, the pathologists were given the option to click on any of the detected high power field regions to view the area at high resolution (i.e. 40x).
While accuracy is certainly the first quality we want the classifier to have in real diagnosis and prognosis application, it is also important to be able to interpret it and understand what the classification is based on.
To understand how the classification scheme works, consider the simulated gene expression data in Figure 1.
The goal of this narrative review was to provide a framework from which to understand the historical progression of the classification and grading of muscle injuries.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com