Fraud detection, manufacturing, Monitoring machines in data center
- Very small number of positive examples(y=1).(0-20 is common)
- Large number of negative(y=0) examples.
- Many different "types" of anomalies. Hard for any algorithm to learn from positive examples what the anomalies look like.
- Future anomalies may look nothing like any of the anomalous examples we've seen so far.
Supervised Learning
Email spam, weather prediction, cancer classification.
- Large number of positive and negative examples.
- Enough positive examples for algorithm to get a sense of what positive example are like, future positive examples likely to be similar to ones in training set.
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