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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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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…
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### Deep Learning for Imbalance Classificaition
#### Survey
1. [A systematic study of the class imbalance problem in convolutional neural networks
Cost-Sensitive](https://arxiv.org/pdf/1710.05381.p…
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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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We are currently using xgboost 1.6.2 and are trying to upgrade to 2.1.1. On the way through the versions, we observed the following prediction time averages:
1.6.2: 15ms
1.7.6: 17ms
2.0.3: 43ms
…
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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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My data set is imbalanced; I understand from the [Interpreting Tree Ensembles with inTrees](https://arxiv.org/pdf/1408.5456v1.pdf) that Error = accuracy.
In an imbalanced set Error rate is not ver…
ghost updated
6 years ago
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# Building a multiclass classification model | Practical Cheminformatics
Data cleaning, adding structures to PubChem data, building a multiclass model, dealing with imbalanced data
[https://patwalte…
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Problem Description:
Fraudulent transactions are a major threat in banking and insurance, causing substantial financial losses and damaging customer trust. Swift, accurate fraud detection is essentia…