You have customer records with no labels and want to find natural groups. Which approach fits?
Why D is the answer
Unsupervised clustering
With no labels, clustering finds structure in the data. Classification needs labeled examples.
In detail
- No labels and a search for groups means unsupervised clustering.
- A person still has to name the groups.
Use case
An illustrative scenario, not a real incident.
A shop has purchase history but no labels. Clustering finds groups such as bargain hunters and regular buyers.
See it in different ways
flowchart TD
A{"What does the data look like?"} -->|"Labeled examples"| B["Supervised: classification, regression"]
A -->|"No labels"| C["Unsupervised: clustering"]
A -->|"Rewards from actions"| D["Reinforcement learning"]mindmap
root((Machine learning))
Supervised
Regression: a number
Classification: a label
Unsupervised
Clustering
Reinforcement
Learns from rewardsflowchart TD
Q{"Is the data labeled?"}
Q -->|"Yes"| A0["Supervised learning"]
Q -->|"No"| A1["Unsupervised learning"]Key point: No labels points to unsupervised learning.
Why the other options are wrong
- A. Reinforcement learning
- Reinforcement learning uses rewards.
- B. Supervised classification
- Classification needs labels.
- C. Regression
- Regression predicts a number from labeled data.