Explain the difference between supervised learning and unsupervised learning in machine learning.

Supervised Learning vs. Unsupervised Learning in Machine Learning

Supervised learning and unsupervised learning are two key approaches in machine learning with distinct differences:

Supervised Learning:

In supervised learning, the algorithm is trained on a labeled dataset, where each input example is paired with its corresponding output label. The goal is to learn a mapping function from inputs to outputs, allowing the algorithm to make predictions on unseen data. The model is guided by the provided labels, making it a form of "learning with supervision."

Examples of supervised learning algorithms include linear regression, decision trees, and support vector machines.

Unsupervised Learning:

On the other hand, unsupervised learning deals with unlabeled data, where the algorithm needs to find inherent patterns and relationships within the dataset without explicit guidance. The aim is to discover hidden structures or groupings in the data without any predefined labels.

Clustering, dimensionality reduction, and association rules are common techniques used in unsupervised learning.

In summary, the main difference between supervised and unsupervised learning lies in the presence (supervised) or absence (unsupervised) of labeled data to guide the learning process.