Comments (3)
Hi.
If you're assuming a constant cost between errors, it is the same doing a balancing of the input dataset than adjusting the threshold doing the cost.
However, if the costs are example-dependent, balancing the dataset does not give you optimal results.
Edit: I've done some Cross Validation to check different C and max_iter but it seems like the best savings score I can get it 0 (with the worst being -12).
That look quite suspicious.
from costsensitiveclassification.
Thanks for the response @albahnsen !:)
Can I confirm the columns of the cost - matrix are?
false positives, false negatives, true positives and true negatives
When I print out the model history (view of the iterations), it suggests the cost per example for the best model is: $0.805161. However, when I manually get the savings score
cost, cost_base, savings_p = savings_score(y_vec, train_predictions, cost_mat)
The cost per example much higher at the cost per alert and the model predicts all fraud.
C= 1.0 - no regularization because I was suspicious about the loss function.
from costsensitiveclassification.
The cost-matrix and loss function appear fine so the problem is with the optimisation of the function.
Now the reason I suggested downsampling is because whereas you had 0.5% true fraud in your example, my example is more like 0.05% or worse. =( Therefore the optimisation tends to converse to predicting a single class. This is not ideal.
from costsensitiveclassification.
Related Issues (20)
- multiclass classification example?
- Push to PyPi HOT 2
- `load_bankmarketing` no longer works HOT 2
- costcla.model is not working with latest version of sklearn HOT 4
- Update example notebook to work with latest version of sklearn HOT 1
- Update tutorial link in README HOT 1
- I want to draw a cost-sensitive decision tree, but it has an error. HOT 1
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- Visualize features being used by CostSensitiveDecisionTreeClassifier HOT 1
- Calculation of cost saving HOT 2
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- pyea imports
- Problem with imports from pyea HOT 3
- getting error: 'bool' object has no attribute 'astype'
- ModuleNotFoundError HOT 2
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- Imbalanced prediction on obesity datasets using cost sensitive algorithms??
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- Add a Conda Forge Package
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from costsensitiveclassification.