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Loss functions for classification
- Value
- 50 W
- Attack
- 725
- Defence
- 580
- Players
- 0
In machine learning and mathematical optimization, loss functions for classification are computationally feasible loss functions representing the price paid for inaccuracy of predictions in classification problems (problems of identifying which category a particular observation belongs to).
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