Which of the following do supervised AI learning models PRIMARILY use to train algorithms?

The correct answer is C. Labeled data sets. Supervised learning is defined by training on labeled datasets-each input example is paired with a known, correct output (the label). The algorithm learns to map inputs to outputs by minimizing prediction error against these ground-truth labels. Unlabeled datasets (A) are the dom

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Question

Options

AUnlabeled data sets
BClustered data sets
CLabeled data sets
DRandomized data sets

How the community answered

(32 responses)

A
3% (1)
B
3% (1)
C
91% (29)
D
3% (1)

Explanation

Supervised learning is defined by training on labeled datasets-each input example is paired with a known, correct output (the label). The algorithm learns to map inputs to outputs by minimizing prediction error against these ground-truth labels. Unlabeled datasets (A) are the domain of unsupervised learning (e.g., clustering, dimensionality reduction). Clustered datasets (B) are an output of unsupervised learning, not an input for supervised training. Randomized datasets (D) have no meaning as a training paradigm; randomization may apply to data splitting (train/test) but does not define supervised learning.

Topics

#Supervised Learning#Machine Learning Training#Labeled Data#AI Fundamentals

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