All Questions
15 questions
0
votes
1
answer
19
views
SVC labels entire sample majority class, even after using ADASYN
I have an imbalanced sample (850 in group X vs 100 in group Y). I am trying to predict group membership using support vector classifcation. I am using 'Adaptive Synthetic' (ADASYN) to oversample the ...
- 103
2
votes
2
answers
2k
views
How to calculate accuracy of an imbalanced dataset
I like to understand what is the accuracy of an imbalanced dataset.
Let's suppose we have a medical dataset and we want to predict the disease among the patients. Say, in an existing dataset 95% of ...
- 361
0
votes
1
answer
83
views
Do I need to use AUPRC for reporting classification results on an imbalanced dataset when the model was trained using upsampling and CV
I am working on a binary classification problem which dataset has about 5% of positive class samples. I split the dataset, 70% for training and 30% for testing. I used the test data only once for ...
- 1
0
votes
1
answer
123
views
How to effectively evaluate a model with highly imbalanced and limited dataset
Most data imbalance questions on this stack have been asking How to learn a better model, but I tend to think one other problem is How do we define "better" (i.e. fairly evaluate the learned ...
- 23
0
votes
1
answer
70
views
Give more weight to features based on distribution plot
I have a task to predict a binary variable purchase, their dataset is strongly imbalanced (10:100) and the models I have tried so far (mostly ensemble) fail. In ...
- 101
3
votes
1
answer
810
views
What does IBA mean in imblearn classification report?
imblearn is a python library for handling imbalanced data. A code for generating classification report is given below.
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- 153
1
vote
2
answers
789
views
Cross validation schema for imbalanced dataset
Based on a previous post, I understand the need to ensure that the validation folds during the CV process have the same imbalanced distribution as the original dataset when training a binary ...
- 755
11
votes
3
answers
7k
views
For imbalanced classification, should the validation dataset be balanced?
I am building a binary classification model for imbalanced data (e.g., 90% Pos class vs 10% Neg Class).
I already balanced my training dataset to reflect a a 50/50 class split, while my holdout (...
- 755
3
votes
1
answer
98
views
The most informative curve for imbalance datasets
For the imbalanced datasets:
Can we say the Precision-Recall curve is more informative, thus accurate, than ROC curve?
Can we rely on F1-score to evaluate the skillfulness of the resulted model in ...
- 248
4
votes
1
answer
1k
views
Why is oversampling outperforming class weight?
I have a dataset that is highly imbalanced. One class has 412 (class 0) samples while the other has 67215 (class 1) samples. For its classification, I am using MLP. When I use class weight of 165 for ...
- 1,161
1
vote
2
answers
290
views
Preferred approaches for imbalanced data
I am building a binary classification model with imbalanced target variable (13% Class 1 vs 87% class 0). I am considering the following three options to handle the data imbalance
Option1: Create a ...
- 755
0
votes
4
answers
10k
views
How to find whether a dataset is blanced or imbalanced?
I have few dataset to experiment classification(Multi-class). These datasets are about 400GB. I wanted to know whether the dataset is balanced or imbalanced. How to know that dataset is balance or ...
- 11
4
votes
3
answers
8k
views
How to Split And Resample Imbalanced Dataset Into Train, Validation and Test
I want to understand how to split the imbalanced data set with a binary target variable where 87% of the samples are negative and 13% of the samples are positive. Now, I know that you should always ...
1
vote
2
answers
910
views
A robust metric in the presence of class imbalance
When evaluating the performance of a multiclass classification problem, on a highly imbalanced dataset, what is the most robust metric for this purpose?
I read a paper that states:
"Average ...
- 611
0
votes
2
answers
210
views
Dealing with the test set of imbalanced data
I am working on a problem dealing with unbalanced data that has a very specific request.
I would like to know the following:
When I have an imbalanced dataset and I do train test split, the test ...
- 725