AWS Certified Cloud Practitioner
Get started today
Ultimate access to all questions.
Deep dive into the quiz with AI chat providers.
We prepare a focused prompt with your quiz and certificate details so each AI can offer a more tailored, in-depth explanation.
A company wants to automatically label customer reviews as positive, negative, or neutral. They already have 10,000 reviews with known sentiment labels.
Which type of learning algorithm is most appropriate?
Real Exam
Community
RRitesh
A
Unsupervised Learning
B
Supervised Learning
C
Reinforcement Learning
D
Self-supervised Learning
Explanation:
Explanation
This is a supervised learning problem because:
-
Labeled Data Available: The company has 10,000 reviews with known sentiment labels (positive, negative, or neutral).
-
Classification Task: The goal is to classify new reviews into predefined categories.
-
Training with Labels: Supervised learning algorithms learn from labeled examples to make predictions on new, unseen data.
Why other options are incorrect:
-
A) Unsupervised Learning: Used when there are no labels, and the algorithm tries to find patterns or groupings in the data.
-
C) Reinforcement Learning: Involves an agent learning through trial and error by receiving rewards or penalties for actions in an environment.
-
D) Self-supervised Learning: A subset of unsupervised learning where the data itself provides supervision, typically by creating pseudo-labels from the data structure.
Key Takeaway: When you have labeled data and want to predict categories or values for new data, supervised learning is the appropriate approach.
Comments
Loading comments...