Do you utilize active learning?
In our latest piece of content, we’re talking about what active learning is, how it works, and why your dataset may need it. We’ll walk through an example using soccer video data to discuss a common query method: uncertainty sampling based on model confidence. We’ll discuss some of the advantages of active learning.
Active learning is a methodology used in machine learning that helps focus data labeling on instances in your data set that will drive the greatest value for your model. Through a variety of algorithms and processes, models are able to identify subsets of valuable data, refer these subsets to human annotators, trigger model retraining with the newly labeled information, and drive greater machine learning model accuracy with less human labeling overall.
Active learning can be a useful tool at many stages of model development, and it is a powerful example of how models, sampling mechanisms, and human annotators can work together quickly and intelligently to produce quality results.
We lay out the advantages of active learning namely,
- Its usefulness for large datasets
- Drive accuracy and performance for special cases
- How it saves you and your team time, money and human effort
The example we discuss demonstrates a common query method: uncertainty sampling based on model confidence. The result is better inferences on previously rare classes, but other methods can maximize model change, minimize generalization error, and quickly identify novel classes. These can be used individually or in concert. Consider also “query by committee”, where sampling is based on the level of disagreement from multiple models.
Dive in now to explore what active learning is and how we do it at Alegion.