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How to correctly select a sample from
a huge dataset in machinelearning
Choosing a small, representative dataset
from a large population can improve model
training reliability
In machine learning, we often need to train a model with a very
large dataset of thousands or even millions of records. The higher
the size of a dataset, the higher its statisticalsignificance and the
information it carries, but we rarely ask ourselves: is such a huge
dataset reallyuseful? Or we could reach a satisfying result with a
smaller, much more manageable one? Selecting a reasonably small
dataset carrying the good amount of information can really make us
savetime and money.
Gianluca Malato
F
o
ll
ow
Mar 28
·
6 min read
. . .
Photo by Lukas fromPexels
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