What is the difference between supervised and unsupervised learning, and when would you use each approach in a machine learning project?

The Difference between Supervised and Unsupervised Learning in Machine Learning

Supervised and unsupervised learning are two fundamental approaches in machine learning. The difference between them lies in the presence of labels or target output during the training phase. In supervised learning, the model is trained on a labeled dataset, where each input is associated with a corresponding output. The algorithm learns to map inputs to correct outputs based on that labeled data.

On the other hand, unsupervised learning deals with unlabeled datasets. The model must infer structure or patterns from the input data without explicit guidance. The goal is to explore the data and find hidden insights, clusters, or underlying relationships.

When to use each approach:

• Supervised Learning: It is commonly used when the desired output is known, and the task involves prediction or classification. Supervised learning is suitable for scenarios where you have labeled training data and want the model to learn the mapping between inputs and outputs.
• Unsupervised Learning: This approach is preferred when labelled data is scarce or unavailable and when the objective is to explore and uncover hidden patterns within the data. Unsupervised learning is beneficial for tasks like clustering, dimensionality reduction, and anomaly detection.

Deciding between supervised and unsupervised learning depends on the specific goals of a machine learning project and the nature of the available data.

In conclusion, supervised learning is ideal for tasks requiring labeled data and clear predictions, while unsupervised learning is suitable for exploring data structures and identifying patterns when labeled data is limited or unnecessary.