Linked Questions

15
votes
3answers
19k views

SVM for unbalanced data

I want to attempt to use Support Vector Machines (SVMs) on my dataset. Before I attempt the problem though, I was warned that SVMs dont perform well on extremely unbalanced data. In my case, I can ...
34
votes
3answers
2k views

What is the root cause of the class imbalance problem?

I've been thinking a lot about the "class imbalance problem" in machine/statistical learning lately, and am drawing ever deeper into a feeling that I just don't understand what is going on. First let ...
25
votes
4answers
3k views

What problem does oversampling, undersampling, and SMOTE solve?

In a recent, well recieved, question, Tim asks when is unbalanced data really a problem in Machine Learning? The premise of the question is that there is a lot of machine learning literature ...
13
votes
2answers
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 ...
2
votes
2answers
3k 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 ...
4
votes
1answer
1k 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: ...
1
vote
1answer
2k 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 ...
1
vote
0answers
971 views

Why would random forest perform bad on unbalanced class

There is a huge number of posts saying that an imbalanced classes are bad. And only half explains it in terms of recall-presicion scores, meaning that accuracy can be high but F1 score low. What I ...
1
vote
1answer
584 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 ...
1
vote
1answer
599 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 ...
1
vote
3answers
256 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%...
1
vote
0answers
667 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 ...
1
vote
1answer
436 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, ...
1
vote
3answers
291 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 ...
2
votes
0answers
277 views

How to work with unbalanced datasets in generalized Boosted Regression Models using gbm in r?

I'm using generalized boosted regression models to explore what is the contribution of 20 independent environmental variables (x1, x2, ...., x20) to the explanation of the variability of the dependent ...

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