Frequently Asked Data Science Interview Question:

Question: Can you explain the difference between supervised and unsupervised learning?

Supervised Learning:

In supervised learning, the algorithm is trained on a labeled dataset. This means that the input data has corresponding output labels that the model learns to predict. The goal of supervised learning is to map input data to the correct output label.

Unsupervised Learning:

In unsupervised learning, the algorithm is trained on an unlabeled dataset. This means that the input data does not have corresponding output labels. The goal of unsupervised learning is to identify patterns or relationships within the data without explicit guidance.

Understanding the differences between supervised and unsupervised learning is crucial for data scientists as it helps determine the appropriate approach for a given problem.