February 2018
Intermediate to advanced
378 pages
10h 14m
English
Content preview from Machine Learning with Swift
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Batch effects
Usually, if you have to label a big dataset manually, you split it into manageable batches. Several people can then work in parallel on different portions. The problem here is that each of those people will introduce a different amount of variability in his/her batch. This is especially the case when subjective opinions are involved, such as "Is this movie review slightly positive or rather neutral?"
Batch effect is also a common problem for datasets that were compiled from several different sources. In many cases, batch effects become apparent when you plot the data obtained from different sources separately.
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