What is supervised and unsupervised learning

Question

Accepted Answer

Supervised learning involves training a model on labeled data, while unsupervised learning involves finding patterns in unlabeled data.

Supervised learning requires a target variable to be predicted, while unsupervised learning does not.
Examples of supervised learning include classification and regression tasks, while examples of unsupervised learning include clustering and dimensionality reduction.
In supervised learning, the model learns from the labeled data provided during training, while in unsupervised learning, the model discovers patterns in the data without guidance.
Supervised learning is more common in applications where the goal is to predict an outcome based on input data, while unsupervised learning is used for tasks such as grouping similar data points together.

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