TRI-TRAIN

Tri-Train is a machine learning technique used to improve the performance of models when there is limited labeled data. It involves training three separate models on different subsets of data. These models then exchange and evaluate their predictions on unlabeled data; if two models agree on a prediction, that data point is added to the training set for the third model. This process leverages consensus to generate more reliable labeled examples from unlabeled data, ultimately enhancing the overall accuracy and robustness of the models without extensive manual labeling.