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G-mean is widely used in classification problems with imbalanced data and has proven to be worthful metric.
I suggest You to add it to the Catboost library.
Unn20 updated
9 months ago
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Hello,
Forgive me if this request has already been made but I could not find it.
The idea is that the ordinary Brier Score in cases of imbalanced datasets may lead to miscalibration of the mino…
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## 🚀 Feature
Area Under ROC with log transformed FPR.
### Motivation
LogAUC is similar to AUROC and is a popular and effective metric to evaluate classification models on very imbalanced data…
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Referring to https://arxiv.org/abs/1705.02315
implemented the W-CEL Loss to compensate for class imbalance in the MultiLabel Classification problem.
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A cfg parameter called "counters_per_class" exists for Object Detection to tackle imbalanced data. Is it possible to add this feature for classification purposes?
And if not, could you guide me on ho…
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Hi,
I have an image classification problem with high class imbalance. In the original Caffe framework, there is an [InfogainLossLayer](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1Infogain…
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https://www.analyticsvidhya.com/blog/2017/03/imbalanced-classification-problem/?utm_source=feedburner&utm_medium=email&utm_campaign=Feed%3A+AnalyticsVidhya+%28Analytics+Vidhya%29
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The dataset is greatly imbalanced (there are more "non outbreak" occurrences than there are "outbreaks") so we'll have to deal with accounting for this before training the ML model, while also represe…
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I have an imbalanced dataset and I want to use DiscreteBCQ for training an agent. My problem is that the imitator performance is not good because the dataset is imbalanced and the classification algor…
N-AtR updated
3 years ago
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I have CT dataset with high class imbalance for binary classification, where only 1% of my 3D CT scan is to be segmented. How do i choose learning rate, loss and epochs?