Semi-supervised learning
Traditional supervised learning relies on a fully labeled dataset, which can be time-consuming and costly to obtain. Semi-supervised learning, on the other hand, allows us to leverage both labeled and unlabeled data to train models and make predictions. This approach offers a more efficient way to label data and improve model performance.
Semi-supervised learning is particularly useful when labeled data is scarce or expensive to obtain. It allows us to make use of the vast amounts of readily available unlabeled data, which is often abundant in real-world scenarios. By leveraging unlabeled data, semi-supervised learning offers several benefits:
- Cost-effectiveness: Semi-supervised learning reduces the reliance on expensive manual labeling efforts. By using unlabeled data, which can be collected at a lower cost, we can significantly reduce the expenses associated with acquiring labeled data.
- Utilization of large unlabeled datasets: Unlabeled data...
