The phrase "assumed features" is correct and usable in written English.
It can be used when discussing characteristics or attributes that are taken for granted or presumed to be present in a particular context.
Example: "The software update includes several assumed features that users expect based on previous versions."
Alternatives: "presumed characteristics" or "expected attributes".
Furthermore, we shed new insight into the mechanics of multi-stable laminates, showing that the multi-stability and snapping behaviour of these structures is much richer than previously assumed, featuring many unstable post-buckling branches and localised regions of stability.
We rarely experience the local community accountability that was an assumed feature of life in previous generations.
On the other hand, such a normal distribution assumed feature selection method can not apply to the RNA-Seq and MiRNA-Seq data directly, because these data are not normally distributed.
What I didn't see were all the people who might celebrate my death, but that's all changed with the skeleton, who assumes features at will.
In different microenvironments, human γδ T cells assume features reminiscent of classical Th1, Th2, Th17, NKT and regulatory T cells, showing diverse biological functions.
These data are the first recordings from aged hair cells and show that the damaged cochlea assumes features reminiscent of development.
It assumes features to be independent and, thus, allows a straightforward decomposition of a prediction into the individual contributions of each feature.
Indeed, the HUVECs monolayer displayed tight junctions further supporting the idea that in coculture models noncerebral endothelial cells may be induced to assume features typical of the cerebral endothelium [ 18– 20, 20– 20].
One is to fit the high-dimensional data, but using simple models that restrict complexity such as naive Bayes models that assume features are conditionally independent or even simpler models that share some parameters across classes (Novovicova et al, 1996).
Existing efficient feature selection algorithms (feature ranking methods) usually assume feature independence, and assign a discriminative score to each feature.
