When should you use semi-supervised learning? (Select two.)

When should you use semi-supervised learning? (Select two.)
A . A small set of labeled data is available but not representative of the entire distribution.
B . A small set of labeled data is biased toward one class.
C . Labeling data is challenging and expensive.
D . There is a large amount of labeled data to be used for predictions.
E . There is a large amount of unlabeled data to be used for predictions.

View Answer

Answer: CE

Explanation:

Semi-supervised learning is a type of machine learning that uses both labeled and unlabeled data to train a model.

Semi-supervised learning can be useful when:

Labeling data is challenging and expensive: Labeling data requires human intervention and domain expertise, which can be costly and time-consuming. Semi-supervised learning can leverage the large amount of unlabeled data that is easier and cheaper to obtain and use it to improve the model’s performance.

There is a large amount of unlabeled data to be used for predictions: Unlabeled data can provide additional information and diversity to the model, which can help it learn more complex patterns and generalize better to new data. Semi-supervised learning can use various techniques, such as self-training, co-training, or generative models, to incorporate unlabeled data into the learning process.