X @Avi Chawla

Active Learning Methodology - Active learning is presented as an efficient method for building supervised models with unlabeled data [1][4] - The process involves iteratively training a model, identifying low-confidence predictions, and labeling them with human input to improve model performance [2][3][4] - The methodology emphasizes the importance of accurate confidence measure generation for effective training [5] Model Building and Refinement - The initial step involves manually labeling a small percentage of the data to create a seed dataset [2] - Probabilistic models are recommended for confidence level determination, using the gap between the top probabilities as a proxy [3] - Cooperative learning, a variant of active learning, utilizes high-confidence data by incorporating the model's predictions as labels [5] Application and Considerations - Active learning can save significant time when building supervised models on unlabeled datasets [4] - The accuracy of confidence measures is critical, as errors can negatively impact subsequent training steps [5]