| Aspect | Supervised Learning | Unsupervised Learning |
|---|---|---|
| Task | Predicts output labels for input data | Finds patterns and relationships in input data |
| Input-Output Data | Requires labeled data (input-output pairs) | Works with unlabeled data (no output labels) |
| Examples | Image classification, sentiment analysis, speech recognition | Clustering, dimensionality reduction, anomaly detection |
| Training Process | Algorithm learns from labeled data with known outcomes | Algorithm learns patterns from unlabeled data |
| Evaluation Metrics | Accuracy, precision, recall, F1-score | Internal metrics like clustering quality, silhouette score |
| Model Interpretation | Can provide insights into decision-making processes | Limited interpretability due to lack of labeled data |
| Common Algorithms | Linear Regression, Decision Trees, SVM, Neural Networks | K-Means, DBSCAN, Principal Component Analysis (PCA) |
| Use Cases | Predictive modeling, classification tasks | Data exploration, feature learning, anomaly detection |
| Loss Function | Uses various error functions to measure prediction accuracy | Not applicable as there are no target labels to compare |
| Example Formula | Output = f(input_features) | Clustering or reduced representation of input data |
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