What characterize unlabeled examples in machine learning

Solution(By Examveda Team)

What characterizes unlabeled examples in machine learning?

Option A: There is no prior knowledge
Unlabeled examples in machine learning typically do not have associated target labels or outcomes. This means there is no prior knowledge or information about the specific categories or values these examples belong to. So, Option A accurately characterizes unlabeled examples.

Option B: There is no confusing knowledge
Option B, stating "there is no confusing knowledge," does not adequately describe unlabeled examples in machine learning. It does not address the absence of labels or the lack of prior knowledge about these examples.

Option C: There is prior knowledge
Option C is not an accurate description of unlabeled examples. Unlabeled examples are typically characterized by the absence of prior knowledge or labels.

Option D: There is plenty of confusing knowledge
Option D, mentioning "plenty of confusing knowledge," is not an appropriate characterization of unlabeled examples. Unlabeled examples are typically blank slates without known categories or values.

In summary, unlabeled examples in machine learning are characterized by the absence of prior knowledge or target labels, as described in Option A.