All Questions
Tagged with class-imbalanceimbalanced-learn
34 questions
0votes
1answer
40views
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
4votes
0answers
69views
How do you know that your classifier is suffering from class imbalance?
Inspired by @Dave's question "Why does data science see class imbalance as a problem for supervised learning when statistics does not?", I am re-posting a question I posed on the stats SE to ...
6votes
3answers
296views
Reproducible examples where balancing the training data demonstrably improves accuracy
I asked this question on the Statistics SE, but there were no answers, even when a modest bonus was available, so I am asking here to see if any examples can be given. I have been looking into the ...
1vote
1answer
1kviews
I used SMOTE-ENN to balance my dataset and it improved the performance metrics, but how can I be sure it's not overfitting?
The models were evaluated using 10-fold cross validation. foldCount = StratifiedKFold(10, shuffle=True, random_state=1) The models in question are XGBoost. ...
- 15
2votes
2answers
2kviews
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 ...
- 381
0votes
1answer
94views
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
0votes
1answer
130views
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 ...
1vote
1answer
431views
Class imbalance: Will transforming multi-label (aka multi-task) to multi-class problem help?
I noticed this and this questions, but my problem is more about class imbalance. So now I have, say, 1000 targets and some input samples (with some feature vectors). Each input sample can have label ...
0votes
1answer
78views
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
0votes
1answer
78views
Over-sampling when predicting a contionuous variable
Let's say I am predicting house selling prices (continuous) and therefore have multiple independent variables (numerical and categorical). Is it common practice to balance the dataset when the ...
- 9
0votes
1answer
249views
Explaining the logic behind the pipe_line method for cross-validation of imbalance datasets
Reading the following article: https://kiwidamien.github.io/how-to-do-cross-validation-when-upsampling-data.html There is an explanation of how to use ...
- 149
0votes
1answer
2kviews
Handling Imbalanced Datasets in Orange
I work in the medical domain, so class imbalance is the rule and not the exception. While I know Python has packages for class imbalance, I don't see an option in Orange for e.g. a SMOTE widget. I ...
- 11
3votes
1answer
829views
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. ...
- 153
2votes
1answer
3kviews
Using SMOTENC in a pipeline
I am trying to figure out the appropriate way to build a pipeline to train a model which includes using the SMOTENC algorithm: Given that the N-Nearest Neighbors algorithm and Euclidian distance are ...
1vote
2answers
808views
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 ...
