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Hi,
I have two questions:
First : Why didn't you use K Fold Cross Validation?
Second : What is the reason use different learning rate for classifier? Is it for faster convergence?
I am tryin…
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### Use case
common
### Name of resource
SMOTE dataset balancing
### ID
SMOTE_dataset_balancing
### Description
Dataset balancing using SMOTE oversampling technique. A balanced da…
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### pycaret version checks
- [X] I have checked that this issue has not already been reported [here](https://github.com/pycaret/pycaret/issues).
- [X] I have confirmed this bug exists on the [latest…
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Referring to https://arxiv.org/abs/1705.02315
implemented the W-CEL Loss to compensate for class imbalance in the MultiLabel Classification problem.
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**Problem Description:**
The project aims to predict whether diabetic patients will be readmitted to the hospital within 30 days of discharge. Many hospitals struggle with managing diabetes properly,…
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Hi, thank you for your interesting work. I am interested in applying your method to a class-imbalanced problem similar to the credit card dataset in your paper. But when I run your "creditcard.py" cod…
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As far as I can tell the MLP classes don't have support for different class weights which are useful for imbalanced classes. Adding this should be easy as it just comes in in the batch gradient summat…
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Hello,
I am dealing with highly imbalance data and I was wondering if there is an option to set the class weight for the target labels for FFM model training to basically under-sample the negative …
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Dataset | gamma neg | highest map (after 40 epcoh)
-- | -- | --
f/m: 460/694 | 4 | 86.69
f/m: 460/694 | 3 | 87.94
f/m: 460/694 | 2 | 87.67
f/m: 460/694 | 5 | 85.66
f/m: 460/694 | 6 | 84.34
…
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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…