What is the primary difference between supervised and unsupervised learning methods?

The primary difference between supervised and unsupervised learning methods is the presence of labeled data. In supervised learning, the algorithm is trained on a dataset that includes both input data and corresponding output labels. The goal is to learn a mapping function from the input to the output based on the labeled data.

On the other hand, unsupervised learning does not have labeled output data. The algorithm is trained on a dataset that only includes input data, and the goal is to find patterns, relationships, or structures in the data without any specific guidance or supervision.

In summary, supervised learning requires labeled data for training, while unsupervised learning does not.

Supervised Learning:

- Labeled data: Uses a dataset where each input data point has a known target value (label).
- Goal: Train a model to learn the relationship between input features and target values and predict outputs based on unseen data.
- Example: Predicting house prices based on features like square footage and location.

Unsupervised Learning:

- Unlabeled data: Uses a dataset where input data points do not have known target values.
- Goal: Find patterns and structures within the unlabeled data without explicit guidance.
- Example: Clustering customers into groups based on their demographics and purchase behavior.