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What is the difference between batch learning and online learning?
1 Answer
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Batch Learning
In batch learning, the model is trained using the entire dataset at once. After training, the model is deployed and typically does not learn from new data unless it is retrained.
Characteristics:
- Uses all available training data
- Training can be time-consuming
- Requires retraining when new data arrives
Example:
Training a sales prediction model once every month using all historical sales data.
Online Learning
In online learning, the model learns incrementally as new data arrives. It updates itself continuously or in small batches.
Characteristics:
- Learns from data stream by stream
- Adapts to changing patterns
- Suitable for real-time systems
Example:
A fraud detection system that updates its model whenever new transactions occur.
Key Differences
| Feature | Batch Learning | Online Learning |
|---|---|---|
| Training Data | Entire dataset | New data incrementally |
| Updates | Periodic retraining | Continuous updates |
| Resource Usage | Higher during training | Lower per update |
| Adaptability | Less adaptive | Highly adaptive |
| Best For | Static datasets | Real-time or changing data |
In short: Batch learning trains on all data at once, while online learning continuously updates the model as new data becomes available.
