Supervised learning – This model learns from the labeled data and makes a future prediction as output
Unsupervised learning – This model uses unlabeled input data and allows the algorithm to act on that information without guidance.
The difference between supervised and unsupervised machine learning lies in the presence or absence of labeled data during the training process:
- Supervised Learning:
- In supervised learning, the dataset used for training consists of input-output pairs, where each input is associated with a corresponding correct output label.
- The algorithm learns to map inputs to outputs based on this labeled data.
- The goal is to learn a mapping function that can predict the output for new, unseen inputs accurately.
- Examples of supervised learning tasks include classification (e.g., spam detection, image recognition) and regression (e.g., predicting house prices, forecasting sales).
- Unsupervised Learning:
- In unsupervised learning, the dataset used for training contains only input data without corresponding output labels.
- The algorithm tries to find patterns, structures, or relationships in the data without explicit guidance on what to look for.
- It aims to discover the underlying structure of the data or cluster similar data points together.
- Unsupervised learning tasks include clustering (e.g., customer segmentation, document clustering) and dimensionality reduction (e.g., principal component analysis, t-distributed stochastic neighbor embedding).
In summary, supervised learning deals with labeled data and aims to learn the mapping between inputs and outputs, while unsupervised learning works with unlabeled data to uncover patterns or structures within the data itself.