Sentence examples for dataset instances from inspiring English sources
In this work, (y_n) represents the dataset instances, the microalgae.
Machine learning (ML) involves building models from labeled training dataset (instances of texts or sentences) in order to determine the orientation of a document.
As it is difficult to set appropriate D and k values [70, 71], the offline phase is a preprocessing three-dimensional phase that computes all possible combinations of D, k, and entire dataset instances.
Given these different sources of uncertainty in the employed analysis, we could easily suspect a risk of overfitting dataset instances and model initial choices when reporting Kagawa et al.
These procedures included negative case analysis in which the robustness of emerging themes was scrutinized by examining, within the dataset, instances of disconfirming or 'discrediting' data.
The latter can be described as the following three simple steps: 1. Read data from a web address, representing an ot Compound or an ot:Dataset instance; 2.
Read data from a web address, representing an ot Compound or an ot:Dataset instance; Perform processing; store results as ot:Dataset representation (e.g. ot:FeatureValue instances); Write the ot:Dataset RDF representation to an OpenTox data service; return the URI of the resulting dataset.
The process of model creation (e.g. using statistical algorithm to build a model) is initiated by sending a POST command to the algorithm service (example available in the Supporting Information [Additional file 1]), and involves the following steps: 1. Optionally read data from a web address, representing an ot:Dataset instance; 2.
Contradictory data can exist in a single source dataset in instances where there are systematic errors, arbitrary errors or different value representations [5, 6].
They queried the dataset for instances where a tissue is associated with the gene TSPAN6 which expresses a medium and a low expression levels.
In the DEAP dataset, the instances were classified into the high arousal class when arousal rating was higher than 4.5; otherwise, they were placed in the low arousal class.
