imbalanced-learn
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A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning
As title. and I find a method in R as following: https://github.com/paobranco/Pre-processingApproachesImbalanceRegression and paper as : https://www.semanticscholar.org/paper/SMOTE-for-Regression-Torgo-Ribeiro/43cda672b9ac0833086e19c90d42c2c0fbc361c6
#### Describe the solution you'd like In #780 the macro-averaged mean absolute error was proposed as a metric to the library. Using the same rationale as for that feature, I...
#### Is your feature request related to a problem? Please describe I'd like to use a sample fraction (float strategy) with RandomOverSampler and RandomUnderSampler. #### Describe the solution you'd like...
#### Describe the bug Hi.The following is the sampling method of BRF in the paper _Using Random Forest to Learn Imbalanced Data_: ``` For each iteration in random forest, draw...
SMOTE/ADASYN classes currently do not provide a `sample_indices_` attribute since they are generating samples that do not belong to the original dataset. However, we could create a new semantic for...
#### Describe the bug Undersampling with NearMiss version 3 does not work well with sampling_strategy=dictionary. A potential explanation could be that the first step of the algorithm already performs an...
Hi. I see a failure in the doctests when running the test suite for the 0.8.0 release of imbalanced-learn. It looks minor to me, I just wanted to flag it...
New methods
This is a non exhaustive list of the methods that can be added for the next release. Oversampling: - [ ] SPIDER - [ ] MWMOTE - [ ] SMOTE-SL...
Hello, I'm undersampling some imbalanced data with each sample a unique name as index. I don't want to lose the samples' index after undersampling because I'm doing a graph -...
As the BalancedBaggingEnsemble, it could be possible to create a meta-estimator using the BalanceEnsemble sampler.