What is the difference between supervised and unsupervised learning?
Supervised learning and unsupervised learning are two main categories of machine learning algorithms.
Focus Keyword: supervised and unsupervised learning
Supervised Learning:
In supervised learning, the algorithm is trained on a labelled dataset, where each input data point is paired with its corresponding output. The goal is for the algorithm to learn the mapping function from input to output, to make predictions on unseen data accurately.
- Requires labelled data for training
- Predicts outputs based on the provided input-output pairs
- Commonly used for classification and regression tasks
Unsupervised Learning:
In unsupervised learning, the algorithm works on unlabelled data, finding patterns and relationships within the data without explicit guidance. The goal is to explore the data and extract meaningful insights, such as clustering similar data points or dimensionality reduction.
- Works with unlabelled data
- Identifies hidden patterns and structures in the data
- Used for clustering, dimensionality reduction, and anomaly detection
Overall, the key difference between supervised and unsupervised learning lies in the presence of labelled data and the goal of the algorithm—predicting outcomes in the former and discovering patterns in the latter.
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