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Although the dataset is large and noisy, we can successfully retrieve geometrically relevant videos.
Simultaneously, if the dataset is large, the computational expense is often high for the detection approach, which is a huge disadvantage if faster detection is required [65].
Thus for some cheminformatics practitioners even the Naive Bayesian algorithm in its standard form is computationally prohibitive when the dataset is large.
The PBD technique is applicable when the training dataset is large enough to be statistically meaningful (as it is the case of the datasets used in this work), including not only the well-performing UEs but also the problematic UEs.
Regression modelling is usually not advised for skewed data, unless the dataset is large.
Furthermore, when the dataset is large, the computational burden incurred can be substantial.
Similar(35)
While this dataset is larger in terms of the numbers of sound files and semantic tags it is not as rich in terms of semantic information as tags are applied to sounds in a binary fashion by the user community.
The proportion of responses for the estimation dataset is larger than for the validation dataset to enhance model accuracy with a greater number of responses.
Given that Hadoop was designed to digest huge datasets [ 17, 40], we hypothesize in terms of the total CPU time that the Hadoop platform becomes more efficient when compared with the HPC platform when the biological sequence dataset is larger.
However the sample size in this dataset was large.
As the dataset was large, all the potential factors were included in the model.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com