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What is Unsupervised learning?

A machine learning approach that finds groupings or structure in data that has no correct-answer labels. It is used for clustering, which divides similar data into groups (k-means and others), dimensionality reduction, which represents data with fewer variables (principal component analysis and others), and anomaly detection, which finds data that differs greatly from the rest. No labels need to be prepared, but the quality of the result is harder to evaluate.

Japanese name: 教師なし学習

Unsupervised learning appears in 2 practice questions across 1 topic, including Machine Learning Basics.

Field: Machine Learning Basics

Try it: questions about Unsupervised learning

Which of the following is an example of unsupervised learning?

  1. ADividing customers into several groups with similar buying patterns, using only their purchase histories
  2. BPredicting equipment failures from sensor values, using past records of whether failures occurred as the correct answers
  3. CLearning how to choose moves in a game so that the reward obtained by trial and error becomes large
Show answer and explanation

Answer: A. Dividing customers into several groups with similar buying patterns, using only their purchase histories

Unsupervised learning finds groupings or structure in data that has no correct-answer labels. Typical examples are clustering, which divides similar data into groups, and dimensionality reduction, which represents data with fewer variables. Predicting with the correct answer of whether a failure occurred is supervised learning, and learning actions from rewards is reinforcement learning.

Unsupervised learning in the explanations

Questions that mention Unsupervised learning (2)

  1. Which of the following is an example of unsupervised learning?
  2. Which of the following describes principal component analysis (PCA)?

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Related terms

Supervised learning Reinforcement learning Principal component analysis (PCA) Activation function Confusion matrix Convolutional neural network (CNN) Cross-validation Gradient descent k-means clustering Overfitting

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