machinelearning

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Jan Kościałkowski 1 year 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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Kevin Moon 1 year 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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