Machine Learning
What is the type of machine learning algorithms used to draw inferences from labeled data?
Supervised Learning.
Card 1 of 259. Answer: Supervised Learning.
Supervised Learning.
Card 1 of 259. Answer: Supervised Learning.
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Supervised learning. It trains on examples that come with the correct output, the label, and learns a mapping from inputs to outputs that it can then apply to new, unlabeled inputs.
When the label is a category, such as spam or not spam, the task is classification; when it is a number, such as a house price, it is regression. Common algorithms include linear and logistic regression, decision trees, random forests, gradient boosting, support vector machines, k-nearest neighbors and neural networks.
It contrasts with unsupervised learning, which finds structure in unlabeled data (clustering, dimensionality reduction), semi-supervised learning, which mixes a few labels with many unlabeled examples, and reinforcement learning, which learns from rewards instead of labels. Self-supervised learning, used to pretrain large language models, creates its labels from the data itself, for example by predicting the next word.