Linked Questions
101 questions linked to/from When is unbalanced data really a problem in Machine Learning?
13
votes
2
answers
6k
views
Bagging with oversampling for rare event predictive models
Does anyone know if the following has been described and (either way) if it sounds like a plausible method for learning a predictive model with a very unbalanced target variable?
Often in CRM ...
- 8,850
6
votes
3
answers
8k
views
Does oversampling/undersampling change the distribution of the data?
I have an imbalanced dataset (10000 positives and 300 negatives) and have divided this into train and test sets. I perform oversampling/undersampling only on the train set since doing this on the test ...
- 69
5
votes
2
answers
9k
views
Classification algorithms for handling Imbalanced data sets
I’m working on a classification problem where dataset is extremely imbalanced ( roughly 13000 "zero" and 100 "one" responses).
As the first step, I trained a Logistic Regression and changing the ...
- 647
6
votes
1
answer
6k
views
How to balance classification?
I have a binary classification problem, where my training data is 70% positive labeled and 30% negative labelled. I use a logistic loss and it always classifies examples positive on the test data.
...
- 2,077
7
votes
1
answer
4k
views
Does class balancing introduce bias?
I have a data set that is imbalanced, the prediction rate is not much better than the base line without doing any class balance. I have two classes and I can't collect more data.
What I have done:
...
- 505
4
votes
3
answers
2k
views
Machine Learning - How to Sample Test and Training Data for Rare Events
Suppose I have a data set with 1000 observations. I want to train and test a Classification Model to predict a target variable as true or false. However, in my observation set, true occurs only say 10%...
- 251
11
votes
1
answer
1k
views
How does class balancing via reweighting affect logistic regression?
When developing machine learning classifiers, some people upsample or upweight the minority class to achieve a 50-50 balance, claiming that this improves performance. Some statisticians have ...
- 11.1k
6
votes
1
answer
5k
views
When to use stratified k-fold
According to a post on Analytics Vidhya:
Having said that, if the train set does not adequately represent the entire population, then using a stratified k-fold might not be the best idea. In such ...
- 205
3
votes
1
answer
4k
views
Regression on imbalanced data
I want to predict a continuous value between 0 and 1 and the true labels are 99% (out of 100000 samples) zero and rest of them are between 0 and 1. What are the approaches that I can take so that I ...
- 149
8
votes
2
answers
1k
views
Should we really do Re-Sampling in Class Imbalance data?
I have been doing ML for quite some time and I have a thought in class imbalance problems that has bothered me quite a lot.
In problems where we have Imbalanced Dataset (one class is far more frequent ...
- 1,115
3
votes
1
answer
2k
views
Imbalanced Test Data
I have an imbalanced (1:5) training and test set with only two classes and have oversampled the training set with SMOTE so that the class ratio is 1:1. The ML model gives values over 0.7 for accuracy, ...
- 504
3
votes
0
answers
2k
views
Effects of class imbalance on nn batch training
Say I have a binary classification task, where the positive class (1) is only 1% of the whole data set.
Intuitively I can understand why this could be bad for the classifier as the model may learn ...
- 377
3
votes
1
answer
2k
views
Does it make sense to use Focal loss for a tree based classifier like XGBoost?
I am working on a classification problem with an imbalanced data (0.2% of positive class). I've seen people using Focal Loss in deep learning in order to help the model focus on hard examples more. I'...
- 405
2
votes
1
answer
2k
views
Calibration after up and downsampling
I am experimenting with different techniques to deal with imbalanced classes in a classification problem. I am comparing upsampling the minority class with downsampling the majority class. Furthermore ...
4
votes
1
answer
1k
views
Can oversampling be moved outside stratified k-fold CV?
In a binary classification task, I am using imbalanced-learn's implementation of SMOTENC to oversample the positive class of a very imbalanced dataset. The total number of examples is very high, so ...
- 113