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While working on the review of #19411, @glemaitre found out that the permutation importance ranking for different metrics (accuracy, balanced accuracy, precision, recall) could vary a lot on imbalance…
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In binary classification, there are situations where the data are imbalanced and only false positives or only false negatives are costly, we would want to tune the model on simply Precision or Recall.…
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Problem:
Hi team! )
How hard would it be to fix this warning:
**"BalancedAccuracy, BrierScore is/are not implemented for GPU"**
Imbalanced classification is a quite usual task.
MultiRMSE as…
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Thanks for the great library! I'm training with an imbalanced dataset. In time-to-event prediction, is it reasonable to weigh positive examples, as can be done in the classification analog? If so, cou…
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Interesting for plotting and type detection
classification
- breast-w
- balance-scale
- cmc
- kc1
- pc3 (very imbalanced)
- 40979 ('mfeat-pixel' version 3)
- first-order-theorem-proving
- B…
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**Is your feature request related to a problem? Please describe.**
Many time classification goals face imbalanced datasets, so the sklearn precision_score is a nice-to-have metric besides F1 score as…
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We do not have an example dataset that shows why it can be misleading to focus on performance metrics that include true negatives (accuracy and ROC curve) when the classes are highly imbalanced. The …
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Hi!
Great Work you have been doing. I am not a NN expert, but I think that the idea is major for many problems. I would like to use that GNN for testing something. However, due to my imbalanced datas…
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Data inventory from @lakikowolfe 👍 Note data and characteristics of the data
- How the data was used
- dtypes
- missing data?
- Include dummy datasets TA made
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## Class 1
- Commute T…
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**Contributors:** @jey-11 @Muthu2110 @vancheeswaran
**Scope / Problem Definition:**
- Dataset: https://www.kaggle.com/datasets/krishnaraj30/finance-loan-approval-prediction-data?select=train.cs…