Supervised learning from labeled hiring outcomes: Which learning approach best describes the training method?

The human interview and rejection decisions are labels, making this supervised learning.

The question

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1. Unsupervised learning that discovers applicant groups.

   Unsupervised learning finds structure without target labels, whereas this dataset includes interview or rejection labels.
2. Self-supervised learning from masked application fields.

   Self-supervised learning creates training targets from the input data, while this model is explicitly trained against human hiring labels.
3. Reinforcement learning from hiring rewards.

   Reinforcement learning uses feedback linked to an agent’s actions; the scenario provides fixed labeled examples instead.
4. Supervised learning from labeled hiring outcomes. ✓

   Supervised learning learns from examples paired with target labels, here the human interview or rejection decisions.

How to remember it

The human interview and rejection decisions are labels, making this supervised learning.

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