There is one question so deeply ingrained into every data
scientist’s mind that it’s usually the first
thing they ask at the start of a new project: is there any labeled data?
More often than not, the answer is “no” or “a little bit,” followed
by an expectation from the client that your team’s fancy
machine learning models should still perform well. Since training models
on very small datasets does not typically yield good results, one
obvious solution is to annotate more data. However, this takes time and
can be very expensive, especially if each annotation requires domain
expertise to
validate.
Fortunately, there are several methods that are well suited for dealing
with few to no labels! You may already be familiar with some of them,
such as zero-shot or few-shot learning, as witnessed by
GPT-3’s impressive ability to perform a diverse range of
tasks with just a few dozen examples.
In general, the best-performing method will depend on the task, the
amount of available data, and what fraction of that data is labeled. The
decision tree shown in Figure 9-1 can help guide us
through the process of picking the most appropriate method.
Figure 9-1. Several techniques that can be used to improve model performance in the absence of large amounts of labeled data
Let’s walk through this decision tree step-by-step:
1. Do you have labeled data? ...
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