All Questions
Tagged with imbalanced-data or class-imbalance
602 questions
3
votes
1
answer
26
views
When using class weights is bad?
I have a DB with 50 different classes.
One of the classes has x10 more data than the other classes.
Each class has ~20K samples and the 'big' class has ~200K samples
When training classification model ...
- 527
0
votes
0
answers
13
views
How to improve LSTM model performance for weather prediction?
I predict rainfall using observational data. There are a total of 87,070 data samples, but only 1,885 samples have rainfall.
And here is the LSTM model I am using:
...
0
votes
1
answer
19
views
Poor performance for two classer in a multi class classification
I have a multi class classification With 5 classes(tabular data), I used xgboost model, the model score well for 3 classes but poor for the raimainig classes(2 classes), I tried up-sampling and class ...
- 3
0
votes
0
answers
17
views
Using ResNet50 with SE block on imbalanced data - pytorch
I worked with a breast cancer ultrasound image dataset containing 432 benign cases, 210 malignant cases, and 133 normal cases. Initially, I used a pretrained ResNet-50 model, which yielded the ...
-1
votes
2
answers
32
views
Imbalanced class in my dataset
I’m working with an imbalanced dataset to predict strokes, where the positive class (stroke occurrence) is significantly underrepresented. Initially, I used logistic regression, but due to the class ...
0
votes
0
answers
24
views
How to handle imbalanced edge weights in a graph for node embedding and edge weight prediction?
I have an undirected weighted graph where the edge weights represent probabilities. The majority of the edge weights are 1 (which are 7 times more frequent than the second major group of weights). I'm ...
-1
votes
1
answer
21
views
I am getting better results with under sampling compared to weight class modification for a binary classification? what could be the possible reason?
I am getting better results with under sampling compared to weight class modification? what could be the possible reason?
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
0
votes
1
answer
129
views
Machine Learning Binary Classification Model on a Small Tabular Imbalanced Dataset - Improving Performance
I have a dataset that is fairly small (15,000 rows), with 10 features for a model to learn from. It is not possible to increase the size of this dataset. I am using machine learning for binary ...
- 173
0
votes
1
answer
44
views
Is there more to imbalanced classification with XGBoost than simply reweighting the loss function?
I am working on a dataset for fraud detection, which is naturally heavily imbalanced. My classifier is a class weighted XGBoost. In other words, I simply overweight the positive class by tweaking ...
0
votes
1
answer
66
views
Imbalanced Cost-Sensitive Learning Workflow - How to split the data, tune hyperparameters and apply adecision threshold?
I am facing a problem with imbalanced dataset in which I would like to detect the rare event. My questions are more of general strategy about the whole workflow and I would like to hear your thoughts ...
- 3
0
votes
0
answers
58
views
Improving Recall and Precision of the Minority Class with XGBoost to Maximize Profits in Unbalanced Data
The company is interested in identifying profitable customers who are likely to purchase a ticket when given a promotional offer. My goal is to build a model to predict whether a customer will buy a ...
- 1
1
vote
1
answer
107
views
Using a regression model, is it possible to precisely predict "outlier" results based on a highly imbalanced dataset?
Title.
I have a dataset that's highly imbalanced, say the output variable I want to predict is restricted within the range from 0 to 1, but almost all of the datapoints sit around 0.7-0.9, while my ...
- 41
0
votes
0
answers
16
views
Impact of Adding Imbalanced Data on Model Performance for Different Groups
Suppose I initially have a dataset with 50 samples of type A and 50 samples of type B, each with several features. I built a neural network model using this data and recorded the prediction accuracy ...
- 1
1
vote
0
answers
43
views
Class imbalance for binary classification tasks
I am looking to train a binary classifier. Most of my experience so far has been with generative models, not classifiers, so I am wondering with respect to training data, what is a good ratio of 0 and ...
- 21
4
votes
0
answers
68
views
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 ...
- 591
0
votes
1
answer
37
views
Fixing class imbalance vs Over-detecting in test data
In my experiences, binary classifiers tend do better in terms of F1 scores when the class imbalance is at least reduced. However, this leads to over-predicting in the test data.
(Thought) Example: If ...
- 131
3
votes
2
answers
482
views
Preserving / fixing class imbalance
Suppose that I have 2 collection $A$ and $B$ of unlabeled animals that are either dogs or cats.
The dogs in $A$ and the dogs in $B$ are not necessarily identical, other than the fact that they are ...
- 131
2
votes
1
answer
45
views
Techniques for solving the problem with an unbalanced data set
I am trying to solve a problem with an unbalanced data set. I have two classes, one is for patients with risk (1), the other for patients without risk (0). I have a larger number of patients without ...
- 21
2
votes
0
answers
118
views
Using SMOTE Train Model and Optimal Cutoff on Unbalanced Test Data
My original dataset has a binary dependent variable with 3% of the values being one. First, I split the original dataset into training and testing sets using an 80-20 split. Since it includes both ...
- 21
0
votes
0
answers
34
views
Extremely Imbalanced and Gapped Dataset in Regression Problem
Currently I am working with a biological dataset with a range of 0-to-1 to do a multi-task regression with Deep Learning. However, this dataset has an empty gap in the range 0 to 0.2 (however there ...
0
votes
0
answers
45
views
Is balancing imbalanced validation set for retraining model after hyperparameter tuning required?
The following are basic steps to modelling, but would like to ask in the case of imbalanced data, is balancing of train dataset required when retraining model on train + validation set after ...
0
votes
0
answers
24
views
What is the Near-Miss Version 3 undersampling algorithm?
I understand version 1 and version 2 of the Near Miss undersampling algorithm, as described here. However, I don't understand version 3 of this algorithm. I'll appreciate an explanation.
- 185
0
votes
1
answer
21
views
Class imbalance problem in binary classification of ecg and eeg data. I am cross posting it here from stackExchange as per a user suggestion
I have attached the link to the stack overflow question page under. In short it is a Class imbalance problem in binary classification of ecg and eeg data.
https://stackoverflow.com/questions/78232398/...
0
votes
0
answers
36
views
Multiclass PyTorch neural net is stuck predicting 1 class, even with "simple" dataset
I'm trying to predict a class of some data, and am struggling.
So, to debug I created a simple test dataset, yet I am having the same issues. I've tried adding weighting, and lastly adding a column ...
- 1
0
votes
0
answers
11
views
What are the drawbacks of utilizing sample weights in classification tasks?
In classification tasks, especially when dealing with unbalanced data, using sample weights can be beneficial. However, it's not always the default choice in ML libraries like AutoGluon ...
- 1
-1
votes
1
answer
49
views
How to deal with a heavily imbalanced test dataset?
Both my train data and test data were imbalanced. So I tried SMOTE for training.
Before Smote:
...
- 1
1
vote
0
answers
61
views
How to solve imbalanced dataset oversampling problem in multi labels-classes instance segmentation task?
I want to use models YOLOv7-seg for instance segmentation of tree species in images. There are 26 species of trees, and each image may contain multiple species. There is a distinction between dominant ...
- 11
0
votes
0
answers
77
views
PR-AUC vs F1 vs Balanced Accuracy
I'm trying to create a Random Forest Classifier for selecting ~ 700 features.
I have a highly imbalanced dataset to select features from. There are significantly fewer positive cases (1%) compared ...
1
vote
0
answers
49
views
Scaling imbalanced binary features
I am interested in a discussion in encoding and scaling categorical features, notably imbalanced categorical features. The context is neural networks (gbdts should handle this easily). It is known ...
- 2,444
2
votes
1
answer
75
views
Bad metrics results by strong class imbalance in Credit card classification
Hi i'm currently in the process of writing my bachelor's thesis and stuck at a some steps.
I've developed a few ML-Model (XGBoost, (Balanced) Random Forest, ElasticNet,...) on an extreme imbalanced ...
- 21
0
votes
0
answers
41
views
CNN segmentation models: class weights specification on IoU metric
I am building a MANet model using pytorch lightning. For getting the model I use the library segmentation models. As my objective is to do binary semantic segmentation, during the test phase I ...
17
votes
2
answers
831
views
Why does data science see class imbalance as a problem for supervised learning when statistics does not?
Why does data science see class imbalance as a problem in supervised learning when statistics says it is not?
Data science seems to seem class imbalance as problematic and needing special techniques ...
- 4,244
1
vote
1
answer
58
views
How to balance labeled datas and then carry out execution with a certain ratio?
I'm building a binary classification model using a neural network, with python and the libraries tensorflow and keras.
For that I have an unequal amount of labeled data: Around 2'000'000 labeled with <...
- 13
0
votes
0
answers
22
views
How should I design the CNN for classifying the relation between 2 texts (multiple classes)
So I have a task to classify the relation between 2 texts (4 classes possible) and one of the requirements is to preprocess them with TfidfVectorizer or CountVectorizer.
Since every sample has 2 ...
- 11
1
vote
1
answer
57
views
How does oversampling or undersampling approch is going to help during the testing on real time data?
We have a dataset with class A as 10% only and Class B as 90% . Let say we did undersampling or oversampling on training data and we made 50% of class A and 50% of class B. But in reality the data ...
- 15
0
votes
1
answer
455
views
Random Forest overfitting to unbalanced data set
I am working on an unbalanced classification problem. I have have 2000 points which are positive, and 6000 points as -ve (chosen randomly from 100k universe of -ve points universe). Although I have ~...
- 85
0
votes
0
answers
26
views
What was used before data augmentation (e.g. SMOTE) to train ML models on imbalanced data? Please provide citations
I am seriously curious on how imbalance data was treated in machine learning and statistical learning before modern time data augmentation solutions such as SMOTE appeared.
Please provide citations ...
- 226
0
votes
0
answers
34
views
What can I do when validation data and test data has different distribution in imbalance classification?
I am building classification model for bio (scRNA) data. Datasets in this field, for example, dataset A has 1, 2 classes, dataset B has 2, 3 classes kind of that. So I integrated datasets for training ...
0
votes
1
answer
332
views
Is there a way to focus mainly on high precision when fitting a tree model?
I have a dataset with 95% false and 5% true labels, some 200000 samples overall, I'm fitting a LightGBM model. I mainly need to focus on high precision and have low number of false positives, I don't ...
- 1
0
votes
2
answers
403
views
How to improve accuracy on a single class out of 3 classes in model
I am training a classification model with 3 classes using a deep neural network.
The classes have been resampled and balanced.
I have around 600000 samples... equally distributed.
The dataset is also ...
- 125
0
votes
3
answers
245
views
Reduce false positives having imbalanced data
I'm using a DNN-48 having the following scenario:
Features: 8 (48 at the end because I generate conditional sequences of 6 elements each)
Classes: Y=0 (90%), Y=1 (10%)
Precision and recall are good ...
- 3
0
votes
2
answers
269
views
what is a random prediction for class imbalanced data? How can I check if my model is predicting randomly?
Say if you have a balanced dataset, with two classes, if the classification model that we’re training doesn’t learn anything ( suppose the data is random ), the model’s output would be 50% first class ...
6
votes
2
answers
3k
views
Determining whether a dataset is imbalanced or not
Is there a practical threshold to determine whether a dataset is imbalanced or not? i.e. we should packages like imbalanced-learn to do some kind of adjustment like ...
- 231
0
votes
2
answers
517
views
Is there any rationale for performing SMOTE-ENN before train-test-split?
I have created a classification model for predicting data, and the problem is that the two classes are highly imbalanced
I have a problem. I have created a classification model for predicting data, ...
2
votes
2
answers
1k
views
Some simple questions about confusion matrix and metrics in general
I will first tell you about the context then ask my questions.
The model detects hate speech and the training and testing datasets are imbalanced (NLP).
My questions:
Is this considered a good model?
...
- 89
0
votes
1
answer
637
views
How to intrepret low F1 score and high AUC on training set?
I am currently working on a very imbalanced dataset:
24 million transactions (rows of data)
30,000 fraudulent transactions
(0.1% of total transactions)
and I am using XGBoost as the model to predict ...
5
votes
1
answer
585
views
Are imbalanced data problems solvable? [closed]
I am working as a data scientist for the past 2 years where I have worked on problems related to binary classification, revenue prediction etc.
In the past two years, I have had 2 problems that ...
- 2,655
6
votes
3
answers
280
views
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 ...
- 591