Match each machine learning term to its description.
Drag a concept onto its matching description — or click a concept then click the description.
Model trained on labeled data
Model trained on unlabeled data
Agent learns by interacting with environment
Model performs well on training data but poorly on new data
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Supervised Learning: Model trained on labeled data to predict output
In machine learning, supervised learning uses labeled data (inputs mapped to known outputs), unsupervised learning finds patterns in unlabeled data, features are input variables, and labels are the target outputs. Common confusions include mixing supervised/unsupervised definitions or swapping features and labels.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Supervised Learning: Model trained on labeled data to predict output
Why this is correct
Supervised learning uses labeled data where the correct output is known.
Unsupervised Learning: Model finds patterns in unlabeled data
Why this is correct
Unsupervised learning discovers hidden patterns without predefined labels.
Feature: Input variable used for predictions
Why this is correct
A feature is an individual measurable property of the data used as input.
Label: Output variable that model predicts
Why this is correct
The label is the true outcome or target value for a given input.
Supervised Learning: Model finds patterns without labeled data
Why it's wrong here
Incorrect — this describes unsupervised learning, not supervised.
Feature: The output variable the model predicts
Why it's wrong here
Incorrect — the output variable is a label, not a feature.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.