The main key difference between supervised and unsupervised machine learning lies in the presence or absence of labeled training data:
Supervised Learning:
- Definition: In supervised learning, the algorithm is trained on a labeled dataset, which means that the input data is paired with corresponding output labels.
- Objective: The primary goal is to learn a mapping or relationship between the input features and the output labels. The algorithm aims to generalize from the training data to make accurate predictions or classifications on new, unseen data.
- Examples: Classification and regression are common tasks in supervised learning. For instance, predicting whether an email is spam (classification) or predicting house prices based on features like square footage and location (regression).
Unsupervised Learning:
- Definition: In unsupervised learning, the algorithm is given unlabeled data, and its objective is to explore the inherent structure and patterns within the data without explicit guidance.
- Objective: The algorithm discovers relationships, similarities, or clusters in the data without predefined output labels. Unsupervised learning is often used for tasks where the goal is to gain insights into the underlying structure of the data.
- Examples: Clustering and dimensionality reduction are common tasks in unsupervised learning. Clustering involves grouping similar data points together, while dimensionality reduction aims to reduce the number of features while retaining essential information.
In summary, the key distinction is the presence of labeled data in supervised learning and the absence of labeled data in unsupervised learning. Supervised learning is used when the algorithm needs to learn from examples with known outcomes, while unsupervised learning is employed when the goal is to explore the inherent patterns or structure within the data without predefined labels.
Read More...
Machine Learning Course in Pune
