What is supervised vs. unsupervised learning?

Supervised learning and unsupervised learning are two primary approaches in the realm of machine learning, each with distinct methodologies, applications, and outcomes. They are designed to allow computers to learn from data and make decisions or predictions based on that data. Here’s a closer look at each:

### Supervised Learning

In supervised learning, the algorithm is trained on a labeled dataset. This means that each training example is paired with an output label. The supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. This approach is used for:

– Classification tasks: Where the output variable is a category, such as “spam” or “not spam” in email filtering.

– Regression tasks: Where the output variable is a real value, such as “price” or “temperature”.

The main characteristic of supervised learning is that its model requires supervision to learn. The process involves teaching the model to understand which inputs correspond to which outputs. This is akin to learning with a teacher that corrects you until you learn to associate the inputs with the right outputs.

### Unsupervised Learning

Unsupervised learning, in contrast, deals with input data without labeled responses. Here, the system tries to learn without a teacher. It’s left on its own to find structure in its input data. Unsupervised learning can discover hidden patterns in data but doesn’t predict a target outcome. It is primarily used for:

– Clustering: Grouping of