Improve your model’s performance with unlabeled data

An introduction to semi-supervised learning and its applications in unstructured data

Most machine learning problems that data scientists usually solve are either supervised learning (i.e. ground truth or actual labels are available for the observations and the algorithms model the conditional probabilty to accurately predict the ground truth or actual labels) or unsupervised learning (i.e. there is no label per observation and therefore we identify clusters, patterns or reduced latent dimensions among the observations). …

data inductive performance python self-training semi-supervised learning unlabeled-data