Active Learning

Active learning is a powerful technique that can help us automate the labeling process for large datasets. By selecting a subset of the data that is most relevant to the task at hand, active learning can be more efficient than manually labeling every example in a dataset. This can lead to better results and more accurate predictions. In this blog post, I'll walk through the concept of active learning, how it works, and share a step-by-step implementation of how to automate dataset labeling for a text classification task using this method.

What is Active Learning?

Active learning is a machine learning technique that allows us to automatically label data that is not labeled by humans. Instead of labeling every example in a dataset, active learning focuses on a small subset of the data and uses it to train a model. As the model learns from the unlabeled data, it can then be used to predict labels for the remaining data.

The idea behind active learning is that it can be more efficient than manually labeling every example in a dataset. By focusing on a small subset of the data, the model can learn from the unlabeled data more quickly and accurately, leading to better results.

How does Active Learning Work?

Active learning works by selecting a subset of the data that is most relevant to the task at hand. This subset is then used to train a model, which can then be used to predict labels for the remaining data. The process is repeated until all the data is labeled.

There are two main types of active learning:

1. Active Learning with Labeled Data: In this approach, the model is trained on a small subset of the data that is labeled by humans. The model learns from this labeled data and can then be used to predict labels for the remaining unlabeled data.

2. Active Learning without Labeled Data: In this approach, the model is trained on a small subset of the data that is not labeled by humans. The model learns from this unlabeled data and can then be used to predict labels for the remaining labeled data.

If you're interested in more verbose explanation of this repo, check this post from my blog.