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# Tweet summary
PR-curve is more sensitive vs. ROC in imbalanced data set.
# Useful link
https://www.kaggle.com/lct14558/imbalanced-data-why-you-should-not-use-roc-curve
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The dataset attached to the datasets folder needs to be tidied up. Some messages are wrongly labeled as being offensive. Also, the dataset is imbalanced i.e. (more than 80% of the data is one class) a…
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## Feature
Similar to https://github.com/tidymodels/probably/issues/159.
When producing a stacked ensemble of predictions, although the base models may have been trained using importance weights…
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Hi,
I am working on a model with highly imbalanced data which originates from few (as few as 30 observations) observations of species presences and a few thousand randomly selected background obser…
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Imbalanced datasets, where the classes have very different occurrence rates, can show up in large data sets.
There are many strategies for dealing with imbalanced data. http://contrib.scikit-learn.…
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covtype
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#### Describe the issue linked to the documentation
There is some discussion going on about the usefulness of some (if not all) over / under sampling methods implemented in the imbalanced learn pac…
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Hi... Analyzing the results in transformer_result2.csv file, I see that training on imbalanced data is affecting accuracy.
Maybe passing class weights to the model will help achieve better accuracy o…
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# To downsample imbalanced data or not, with #TidyTuesday bird feeders | Julia Silge
A data science blog
[https://juliasilge.com/blog/project-feederwatch/](https://juliasilge.com/blog/project-feeder…
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Description
Problem Statement:
For a safe and secure lending experience, it's important to analyze the past data. In this project, you have to build a deep learning model to predict the chance of de…