| Feature | Supervised Learning | Unsupervised Learning |
|---|---|---|
| Definition | A type of machine learning where the model learns from labeled data, meaning the data has both input and output variables. | A type of machine learning where the model learns from unlabeled data, meaning the data has only input variables. |
| Goal | To learn a mapping function that can predict the output variable for new data based on the input variables. | To learn patterns or structures in the data without the need for labeled data. |
| Input data | Labeled data | Unlabeled data |
| Learning method | Based on labeled data, the model learns to map input variables to output variables. | Based on patterns or structures in the data, the model learns to group or cluster data points. |
| Types | Regression, classification | Clustering, dimensionality reduction |
| Examples | Linear regression, logistic regression, decision trees | K-means clustering, hierarchical clustering, principal component analysis |
| Applications | Predicting continuous values (e.g., stock prices, weather forecasts), classifying data points (e.g., spam detection, image recognition) | Grouping or clustering data points (e.g., customer segmentation, market research), reducing the dimensionality of data (e.g., for visualization or feature selection) |
| Advantages | High accuracy and interpretability | Can handle large and unlabeled datasets |
| Disadvantages | Requires labeled data, which can be expensive and time-consuming to obtain | Can be difficult to interpret the learned patterns or structures |
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