Active Learning Tools and Strategies

In Part 1, we explained what is active learning and gave a good explanation on different types of strategies and main advantages of active learning. In this post, we continue with explaining more precisely the strategies and the main popular tools in this domain. Lets get in 💛

Active learning: strategies for subsampling

Selecting the best query strategy is very important as they decide which data points are informative and should be sent for labeling and used for further training. The efficiency of the active learning pipeline can be determined by how quickly the query strategy can select the most effective sample from the pool of unlabeled data.

• Uncertainty-based Sampling
• Committee-based Sampling
• Diversity Weighted Methods
• Expected Model-change-based Sampling
• Expected Error Reduction
• Stream-based Sampling
• Active Regression
• Bayesian optimization
• Multilabel Strategies

Uncertainty-based Sampling

Uncertainty sampling is a query strategy that selects samples that are expected to reduce the uncertainty of the model the most. The…