libact: Pool-based Active Learning in Python
arXiv:1710.00379
Abstract
libact is a Python package designed to make active learning easier for general users. The package not only implements several popular active learning strategies, but also features the active-learning-by-learning meta-algorithm that assists the users to automatically select the best strategy on the fly. Furthermore, the package provides a unified interface for implementing more strategies, models and application-specific labelers. The package is open-source on Github, and can be easily installed from Python Package Index repository.
References in corpus (1)
Cited by in corpus (10)
- Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
- Optimizing spectroscopic follow-up strategies for supernova photometric classification with active learning
- Small-Text: Active Learning for Text Classification in Python
- DeepAL: Deep Active Learning in Python
- TARexp: A Python Framework for Technology-Assisted Review Experiments
- Extracting localized information from a Twitter corpus for flood prevention
- TAR on Social Media: A Framework for Online Content Moderation
- Active Learning Under Malicious Mislabeling and Poisoning Attacks
- ALE: A Simulation-Based Active Learning Evaluation Framework for the Parameter-Driven Comparison of Query Strategies for NLP
- Active learning of potential-energy surfaces of weakly-bound complexes with regression-tree ensembles