-
Hi zoogzog,
I'm wondering why to apply tencrop technique on testing images. I thought data augmentation techniques should only be applied on training set in order to add diversity of training image…
-
Hi,
I experimented with verifying your approach however I didn't receive such low F1 numbers for few shot. In looking at data_full, banking has the same amount of training samples as all other do…
-
Love the paper.
I've tried it on my own closed domain dataset and achieved poor recall.
```
Role identification: P: 49.30, R: 28.43, F: 36.06
Role: P: 44.41, R: 25.60, F: 32.48
Coref Role iden…
-
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…
-
# Tweet summary
Adjust probability bias due to sampling for imbalanced data
# Useful link
https://pompom168.hatenablog.com/entry/2019/07/22/113433
-
Hi all,
I am wondering if you will add a feature such as handling data imbalance like imbalance measurement or multi-label resampling. I came across this [paper](http://www.sciencedirect.com/scienc…
-
I have a dataset which normally has a binary class in two cases:
case 1:
label = 1 which is the minority data and important
label = -1 which is the majority data and not important
case 2:
lab…
-
Have a look at this [link](https://gridftp.tamucc.edu/fognet/datashare/archive/datasets/24HOURS/TARGET/). Does it look like a balanced training dataset to you? Not sure what VIS (visibility) unit is b…
-
This is a follow-up to the issue raised in https://github.com/scikit-learn/scikit-learn/issues/29554. However, I recall other issues raised for CV estimator in general.
So the context is the follow…
-
# 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