machinelearning

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Arne Schoen 1 week ago

I'd recommend this paper as it touches on the problem you describe and presents two other diagnostic plots which aim at addressing it. PRC ends up being the most informative one in the end anyway. 🙂 https://www.researchgate.net/publication/273155496_The_Precision-Recall_Plot_Is_More_Informative_than_the_ROC_Plot_When_Evaluating_Binary_Classifiers_on_Imbalanced_Datasets

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Sean Borer 1 week ago

Hello, im working on project with high imbalanced data ( fraud predection) based on the client courses ( texts and values) can you advise me to do it well ? Thanks

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Candida Greenholt II 1 week ago

UMAP and t-SNE are both pretty good but they tend to introduce distortions. This blogpost goes over the weaknesses of t-SNE (most of which UMAP inherits): https://distill.pub/2016/misread-tsne/

If you have noisy data, I recommend PHATE which preserves both local and global structure: https://github.com/KrishnaswamyLab/PHATE

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