Summary
In this chapter, we have explored a range of techniques to tackle the challenge of data labeling in regression tasks. We began by delving into the power of summary statistics, harnessing the mean of each feature in the labeled dataset to predict labels for unlabeled data. This technique not only simplifies the labeling process but also introduces a foundation for accurate predictions.
Further enriching our labeling arsenal, we ventured into semi-supervised learning, leveraging a small set of labeled data to generate pseudo-labels. The amalgamation of genuine and pseudo-labels in model training not only extends our labeled data but also equips our models to make more informed predictions for unlabeled data.
Data augmentation has emerged as a vital tool in enhancing regression data. Techniques such as scaling and noise injection have breathed new life into our dataset, providing varied instances that empower models to discern patterns better and boost prediction accuracy...
