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Module 3: Classification and Prediction

20202m

You are given data about seismic activity in Japan, and you want to predict a magnitude of the next earthquake, this is an example of

(i) supervised learning
(ii) unsupervised learning
(iii) serration
(iv) dimensionality reduction

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Answer: (i) supervised learning

Predicting the magnitude (a continuous numeric value) of the next earthquake from seismic-activity data is a prediction/regression task. Because we train a model using historical data that already includes known outcomes (earthquake magnitudes) and then predict a numeric value for new data, it is a supervised learning problem.

Why the others are wrong:

  • (ii) unsupervised learning — no hidden structure/labels are being found; we are predicting a specific numeric value from labelled historical examples.
  • (iii) serration — a typo/nonsense option (no such learning category).
  • (iv) dimensionality reduction — that is a preprocessing/feature-compression technique (like PCA), not the task of predicting a magnitude.

Hence predicting the magnitude of the next earthquake is an example of supervised learning.

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