ALiPy: Active Learning in Python

Understand

Supervised machine learning methods usually require a large set of labeled examples for model training.

• However, in many real applications, there are plentiful unlabeled data but limited labeled data; and the acquisition of labels is costly.
• Active learning (AL) reduces the labeling cost by iteratively selecting the most valuable data to query their labels from the annotator.
• This article introduces a Python toobox ALiPy for active learning.