Comparing active and passive learning
In traditional passive machine learning, models are trained on fixed and pre-existing labeled datasets, which are carefully assembled to include both data points and their respective ground truth labels. The model then goes through the dataset once, without any iteration or interaction, and learns the patterns and relationships between the features and labels. This is the passive learning approach. It’s important to note that the model only trains on the finite data it is provided and cannot actively seek out new information or modify its training based on new inputs. Moreover, the labeled datasets required for a passive learning approach come at a cost.
There are several reasons why labeling is expensive in traditional machine learning:
- Manual labeling requires experts: Accurately labeling data often demands the expertise of domain specialists such as doctors or ecologists. However, their time is limited and valuable, making...
