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CT-AI · Question #92

You have been asked for your opinion on the ML approach to be used for a new age verification system. A training dataset is available with many pictures of faces and the subject's age. When new…

The correct answer is B. Supervised learning classification system that is re-trained based on new data, including whether. A supervised learning classification system is most likely to succeed in this scenario because it involves using labeled data (the pictures of faces with corresponding ages) to train a model. The system can then classify whether a person is under or over 18 years old. The…

Machine Learning (ML)

Question

You have been asked for your opinion on the ML approach to be used for a new age verification system. A training dataset is available with many pictures of faces and the subject's age. When new users sign-up to a mobile app, they have to take a picture of their face. If the system believes they are under 18 years old it will ask them to provide a picture of their passport to prove their age. Which ONE of the following approaches do you expect to be MOST likely to succeed?

Options

  • AUnsupervised learning that identifies clusters in training data that relate to people's ages.
  • BSupervised learning classification system that is re-trained based on new data, including whether
  • CSupervised learning regression system that is re-trained based on new data, including whether
  • DReinforcement learning classification system with a reward function of correct behavior.

How the community answered

(35 responses)
  • A
    9% (3)
  • B
    71% (25)
  • C
    17% (6)
  • D
    3% (1)

Explanation

A supervised learning classification system is most likely to succeed in this scenario because it involves using labeled data (the pictures of faces with corresponding ages) to train a model. The system can then classify whether a person is under or over 18 years old. The approach of re- training based on new data (including whether they later provided ID showing their age) ensures continuous improvement of the model. This method directly aligns with the goal of age

Topics

#supervised learning#classification vs regression#model selection#age verification

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