In classification problem we usually have label data, we train our model with that and predict the class for the test data. However, there is another lane of research on labeling unlabeled data, be it supervised or unsupervised manner. It is an essential task in todays world as unlabeled data is huge compare to the labeled ones. My question here is can we say a classification model with high accuracy can act like a model for labeling unlabeled data? I am talking about domain specific datasets only. Let say medical or consumer review etc. And along with it how do meta-learning works?
Using labeled data optimizing accuracy, though it hard for large databases. For details, a good related article of this query may be https://towardsdatascience.com/authoritative-intelligence-how-data-labelling-increases-the-accuracy-of-ai-models-ca7d58ec8fdc and Preprint Increasing the Speed and Accuracy of Data LabelingThrough an...
Labeled data, used by Supervised learning add meaningful tags or labels or class to the observations (or rows). These tags can come from observations or asking people or specialists about the data.
Classification and Regression could be applied to labelled datasets for Supervised learning.
Machine learning models can be applied to the labeled data so that new unlabeled data can be presented to the model and a likely label can be guessed or predicted.
Unlabeled data, used by Unsupervised learning however do not have any meaningful tags or labels associated with it.
Labeled data: Data that comes with a label.
Unlabeled data: Data that comes without a label.
also look at these links:
https://livebook.manning.com/book/grokking-machine-learning/2-1-what-is-the-difference-between-labelled-and-unlabelled-data-/v-4/26
https://www.baeldung.com/cs/ml-labeled-vs-unlabeled-data
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