A data scientist creating categories and training the algorithm on large data sets is an example of which type of AI model learning technique?

The correct answer is D. Supervised. Supervised learning is defined by the use of labeled training data, where the data scientist explicitly defines the categories (labels) and trains the model to map inputs to those predefined outputs. The model learns from human-provided ground truth. Reinforcement learning (A) in

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Question

Options

AReinforcement
BUnsupervised
CMachine learning (ML)
DSupervised

How the community answered

(40 responses)

A
3% (1)
C
3% (1)
D
95% (38)

Explanation

Supervised learning is defined by the use of labeled training data, where the data scientist explicitly defines the categories (labels) and trains the model to map inputs to those predefined outputs. The model learns from human-provided ground truth. Reinforcement learning (A) involves an agent learning through trial and error by receiving rewards or penalties from an environment-there is no pre-labeled dataset. Unsupervised learning (B) is the opposite of supervised: the algorithm finds patterns and structures in unlabeled data without predefined categories. Machine learning (C) is the broad parent field that encompasses supervised, unsupervised, and reinforcement learning-it is a category, not a specific technique, so it cannot be the precise answer here.

Topics

#Supervised Learning#AI Learning Techniques#Machine Learning Concepts

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