AWS Certified Cloud Practitioner
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You have a dataset with features such as location, size, number of bedrooms, and the corresponding house prices. You need to build a model to predict the price of a new house. Which approach fits best?
Real Exam
Community
RRitesh
0
A
Unsupervised Learning
B
Reinforcement Learning
C
Supervised Learning (Regression)
D
Clustering
Explanation:
Explanation
This is a supervised learning regression problem because:
- Supervised Learning: We have labeled training data where the target variable (house price) is known for each set of features
- Regression: The target variable (price) is a continuous numerical value
- Problem Type: Predicting a continuous numerical output based on input features
Why other options are incorrect:
- Unsupervised Learning (A): Used when there are no labels/target variables, for discovering patterns or groupings
- Reinforcement Learning (B): Used for decision-making problems where an agent learns through trial and error with rewards
- Clustering (D): A type of unsupervised learning used to group similar data points together without predefined labels
Since we're predicting a continuous value (price) based on known input-output pairs, supervised learning regression is the appropriate approach.
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