It involves training a model using both labeled and unlabeled data to improve the accuracy of predictions. It is often used when the amount of labeled data is limited and costly.
It involves training a model using both labeled and unlabeled data to improve the accuracy of predictions. It is often used when the amount of labeled data is limited and costly.