Machine learning is one of the most exciting technologies in use today, and it has the potential to improve the abilities of developers in many ways. Machine learning allows computers to make decisions based on a set of pre-programmed qualifiers and data. This type of technology doesn’t creatively think, but it can emulate thinking in a fairly convincing fashion.

Active learning machine learning involves an algorithm asking questions of a developer or human annotator. In active learning machine learning, a selection of data points that are questionable may be presented for human annotation to help train the machine learning algorithm to identify data and then make decisions about it.

Below are three benefits of active learning in machine learning:

1. Improves Accuracy

Because computers don’t think or learn on their own, the only way to improve accuracy is to train machine learning models on data. This data needs to be annotated, but doing this across large volumes of data is time-consuming and expensive.

Through active learning, only questionable data points can be presented for annotation. This allows algorithms to improve

accuracy since data on the margins that are deemed questionable can be given special attention.

2. Improves Productivity

As mentioned above, training machine learning models on large datasets takes a long time. To train an accurate model, developers may take months or years to get the right data accurately annotated.

By engaging in active learning, machine learning models can be trained more quickly. This then leads to improved productivity for developers as they have more time and resources to devote to other tasks. Additionally, the focus is only placed on training that makes a difference in the project at hand as opposed to training a machine learning model on extraneous data that isn’t relevant to the project.

3. Reduces Cost

Because time is saved using active learning, development teams can also save money. What would normally take many hours of intensive labor can be reduced when using machine learning that relies on active learning. The result is smaller costs for development teams.

Author Resource:-

Emily Clarke writes about tech for automated annotation, AI labeling, data evaluation and more. You can find her thoughts at learning software blog.