Which of the following approaches is best if a limited portion of your training data is labeled?

Semi-supervised learning is an approach that is best if a limited portion of your training data is

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

data to train a model. 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. Semi-supervised

learning can use various techniques, such as self-training, co-training, or generative models, to

incorporate unlabeled data into the learning process.