Active learning is a technique that helps improve the quality of your dataset by selectively choosing the most informative samples for labeling. Instead of randomly labeling a large number of data points, active learning focuses on those that will contribute the most to model performance. Typically, this involves an iterative process where the model is trained on a small, labeled dataset, makes predictions on the unlabeled dataset, and then requests labels for the samples it is most uncertain about. This approach increases the efficiency of labeling efforts, as you are prioritizing the data that will improve your model’s accuracy.
To implement active learning, start by training an initial model using a small, labeled dataset. After training, use this model to make predictions on a larger pool of unlabeled data. You will then need to establish a query strategy to identify which instances are uncertain. Common strategies include uncertainty sampling, where you select data points for which the model has the lowest confidence, or query by committee, which involves using multiple models to gauge disagreement on predictions. For example, if a model is equally likely to classify an image of a cat or a dog, that image would be a good candidate for labeling.
Once you've identified the uncertain samples, label them through manual review or semi-automated techniques, and then add these new labeled samples to your dataset. Re-train your model with the expanded dataset and repeat the process. This iterative cycle can significantly enhance the quality of your dataset by systematically addressing areas where the model struggles, ultimately leading to better model performance in real-world applications. By focusing on the most informative examples, active learning reduces the effort needed for data labeling while maximizing the insights gained from the additional labeled samples.