X @Avi Chawla

Active Learning Overview - Active learning is an efficient method for building supervised models with unlabeled data by incorporating human feedback [1][4] - The core idea involves iteratively training a model, identifying low-confidence predictions, and using human labels to refine the model [2][4] Active Learning Process - The process begins with manually labeling a small portion of the data to create an initial model [2] - The model then generates predictions on the unlabeled data, along with confidence levels for each prediction [3] - Low-confidence predictions are prioritized for human labeling and then fed back into the model for retraining [4] - This iterative process continues until the model achieves satisfactory performance [4] Key Considerations - Generating accurate confidence measures is crucial for the success of active learning [5] - Cooperative learning, a variant of active learning, incorporates high-confidence data by using the model's predictions as labels [5]