What is the difference between supervised and unsupervised learning?
This question checks your fundamental understanding of learning paradigms based on the availability of labeled data.
Why Interviewers Ask This
This is a foundational question to categorize your knowledge. Interviewers ask this to ensure you can distinguish between tasks requiring ground truth labels versus those exploring data structure. It sets the stage for discussing specific algorithms.
How to Answer This Question
Define Supervised Learning as learning from labeled data to predict outcomes (e.g., regression, classification). Define Unsupervised Learning as finding patterns in unlabeled data (e.g., clustering, dimensionality reduction). Provide examples for each to illustrate the difference in goals and data requirements.
Key Points to Cover
- Supervised uses labeled data for prediction.
- Unsupervised finds patterns in unlabeled data.
- Supervised examples: classification, regression.
- Unsupervised examples: clustering, PCA.
Sample Answer
Supervised learning involves training a model on a dataset where the target variable or label is known. The goal is to learn a mapping from inputs to outputs, commonly used for tasks like classification and regression. I…
Common Mistakes to Avoid
- Confusing semi-supervised with unsupervised.
- Giving vague examples without clear distinction.
- Not mentioning the role of labels.
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