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Towards Data Science0 Hot0 comments20 min read
Reducing Human Annotation with ML Active Learning
Active learning reduces the cost of data labeling by having models identify which unlabeled samples are most informative before requesting human annotation. Three query strategies are covered with Python implementations: uncertainty sampling (selecting samples near the decision boundary), diversity-based sampling (using KNN to find sparse, underrepresented regions), and query by committee (training multiple models and selecting samples where they disagree most). Each strategy's strengths and weaknesses are discussed, along with guidance on combining them — starting with uncertainty sampling for early gains, adding diversity sampling to avoid redundancy, and using committee-based methods for noisy decision boundaries. Model stacking is also introduced as a way to combine committee predictions for improved performance.
Source: https://towardsdatascience.com/reducing-human-annotation-with-ml-active-learning. 8sync News only summarizes and links out; content copyright belongs to the authors and original sources.
