In the context of machine learning, what is the main difference between supervised and unsupervised learning in terms of data?
- Feature selection
- Hyperparameter tuning
- Labeled data
- Unlabeled data
The main difference between supervised and unsupervised learning is the presence of labeled data in supervised learning. In supervised learning, the model is trained using labeled data, which means it knows the correct answers. Unsupervised learning, on the other hand, works with unlabeled data, where the model has to find patterns and relationships on its own. Feature selection and hyperparameter tuning are aspects of model training but not the key distinction.
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