Linear discriminant analysis (LDA) is an approach used in supervised machine learning to solve multi-class classification problems. LDA separates multiple classes with multiple features through data dimensionality reduction. This technique is important in data science as it helps optimize machine learning ...
When this procedure is repeated, such as when developing a random forest, numerous bootstrap samples and OOB sets are generated. The OOB sets can be combined into a single dataset, however, each sample is only considered out-of-bag for trees that do not include it in their bootstrap sample...
The main idea is simple. First, train the model on labeled data, then use the trained model to predict labels on the unlabeled data, thus creating pseudo-labels. Further, combine the labeled data and the newly pseudo-labeled data in a new dataset that is used to train the data. I was ...