Describe the difference between supervised and unsupervised learning in machine learning and provide examples of when each type of learning algorithm would be used.

Supervised vs. Unsupervised Learning in Machine Learning

Supervised Learning: Supervised learning is a type of machine learning where the algorithm is trained on labeled data, with input features and corresponding output labels provided. The goal is for the algorithm to learn a mapping function from the input to the output labels, in order to make predictions on new, unseen data. Examples of supervised learning algorithms include linear regression, support vector machines, and neural networks.

When to use Supervised Learning: Supervised learning is typically used when you have a specific target variable or output that you want the model to predict. This type of learning is well-suited for tasks such as classification, regression, and forecasting, where the goal is to predict a specific outcome based on the input features.

Unsupervised Learning: Unsupervised learning, on the other hand, is a type of machine learning where the algorithm is trained on unlabeled data, without any specific output labels provided. The goal of unsupervised learning is to explore the underlying structure of the data or find patterns within the data. Examples of unsupervised learning algorithms include k-means clustering, hierarchical clustering, and principal component analysis (PCA).

When to use Unsupervised Learning: Unsupervised learning is used when you want to discover hidden patterns or groupings within the data, without having predefined labels or outcomes to predict. This type of learning is often applied in tasks such as customer segmentation, anomaly detection, and dimensionality reduction.