Supervised Learning
Supervised learning involves training a model on a labeled dataset, which means that each training example is paired with an output label. The model makes predictions based on the input data, and is then corrected by comparing its prediction to the actual output.
Benefits
- High accuracy
- Predictable outcomes
- Good for specific tasks
Examples
Common applications include image classification, fraud detection, and email filtering.
Unsupervised Learning
In contrast, unsupervised learning deals with datasets that have no labels. The goal is to model the underlying structure or distribution in the data in order to learn more about the data.
Benefits
- No need for labeled data
- Can discover patterns
- Good for exploratory analysis
Examples
This is used in applications such as clustering, recommendation systems, and anomaly detection.
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