Supervised and unsupervised learning are two related types of machine learning. Here’s everything you need to know about supervised vs. unsupervised learning, including how they compare, how they’re different, and the benefits and limitations of each.
Guided vs. unguided learning
Artificial intelligence (AI) programs rely on machine learning algorithms to perform new tasks while absorbing new information. Supervised learning uses sets of data labeled by a human to train the AI. With unsupervised learning, the AI is given raw, unlabeled data, which it must sort through on its own to identify patterns.
Supervised learning requires more human labor, because someone (the supervisor) has to label the training data and test the algorithm. So there is a greater risk of human error.
Unsupervised learning is more computationally expensive and time consuming, but still cheaper than supervised learning because it requires minimal human involvement. That said, it can be difficult for humans to verify the accuracy of the outputs.
