All Questions
Tagged with class-imbalance classification
235 questions
3
votes
1
answer
27
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
1
answer
19
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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
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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
66
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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
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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
0
votes
1
answer
37
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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
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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
0
votes
0
answers
11
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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
0
votes
0
answers
77
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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 ...
2
votes
1
answer
75
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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
17
votes
2
answers
831
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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
57
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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
0
answers
34
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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
637
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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
0
votes
2
answers
532
views
Interpretation of evaluation metrics for an imbalanced dataset
I am currently dealing with a classification problem for a massively imbalanced dataset. More specifically, it is a fraud detection dataset with around 290k rows of data, with distribution of 99.8% ...
1
vote
1
answer
24
views
Movement in cohorts
I am working on a user sales data which gets updated week over week. Based on the sales done in each week, the user is categorized in segment A, B or C. This means size of each segment could change ...
- 31
0
votes
0
answers
97
views
How to handle imbalance in input variables?
Currently working on a finance dataset which has more than 20 input variables with high imbalance.
[Apparently, the target variable is also imbalanced (for this I am currently considering to handle it ...
- 1
3
votes
2
answers
383
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Measure distance between teeth using Machine Learning
I'm a newbie in ML and I have a problem I am stuck on.
I want to train a ML model to recognize dental diagnosis based on photos and x-rays of the patient. Specifically right now, I want to find a way ...
0
votes
0
answers
40
views
Binary Classification: My model classfies most data (95%+) as label 1
I am working with ECGs and trying to use a CNN model to perform binary classification. The goal is to classify 30s ECGs to detect a specific disease. I am using CNN and converting ECGs to images (...
- 11
1
vote
1
answer
269
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If I'm comparing performance between two different datasets should sample and class size be uniform?
If I'm comparing performance between classification models on two different datasets should the number of samples per class, the number of classes, and features per sample be the relatively the same ...
- 13
4
votes
2
answers
2k
views
Flipping the labels in a binary classification gives different model and results
I have an imbalanced dataset and I want to train a binary classifier to model the dataset.
Here was my approach which resulted into (relatively) acceptable performance:
1- I made a random split to get ...
- 43
1
vote
1
answer
69
views
Why is my model overfitting?
I am building a classification model based on some machine performance data. Unfortunately for me, it seems to over-fit no matter what I change. The dataset is quite large so I'll share the final ...
0
votes
0
answers
28
views
Which model to use for multitarget classification with strong class imbalance and many categorical variables?
I have a small dataset 79 observations in 21 variables.
Almost all the variables are categorical variables in the format yes/no or 1/2/3.
I would like to predict jointly three of these variables ...
0
votes
0
answers
44
views
Model with no historical data
I need to develop a new credit default classification model for which there are a lot of features available but very few historical data (because it's a new activity launched by the company I work for)...
- 181
0
votes
1
answer
152
views
Classification on severe Class Imbalance high dimensional data
Dear DataScience Community, I am working on class imbalance tabular data with high-dimension inputs. The tabular data is derived from the satellite data pixels, and I have inflated the train data ...
- 101
3
votes
2
answers
2k
views
Precision, recall and importance of them in the imbalance problem
I have a test dataset. The dataset is an imbalanced dataset. The total training instances for the dataset is 543 among them minority class(yes) is 75 and the majority class(No) is 468. The class of ...
- 361
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
147
views
Top N accuracy for an imbalanced multiclass classification problem
I have a multi-class classification problem that is imbalanced. The task is about animal classification.
Since it's imbalanced, I am using macro-F1 metric and the current result that I have is: ...
- 262
0
votes
0
answers
178
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The paradox of Imbalanced binary classification ¿To do something or to do nothing?
Context: Suppose we are interested in deploy a machine learning model to solve a problem of binary classification; furthermore, assume that the dataset $\mathcal{D}$ for the training of our model ...
- 143
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
4
votes
1
answer
584
views
Zero-shot learning for tabular data?
Can anyone point me to methods for zero-shot learning on tabular data? There is some very cool work being done for zero-shot learning on images and text, but I'm struggling to find work being done to ...
- 73
2
votes
1
answer
835
views
class weighted classification
I am working on my multi-class classification project and I have a question:
I have three classes in proportion: 50%, 47% and 3%. I decided to use ...
- 41
1
vote
0
answers
19
views
Model a classification problem with multiple categorical varialbes as input features only. Diff Model performance
I'm having an input data with 100k rows, 8 input features, I'm trying to predict y (binary 1/0).
But all the X are categorical variables(strictly nominal variables, not ordinal). Some with 8 levels, ...
- 11
3
votes
1
answer
326
views
Area Under the Precision Recall Curve
I have got the following Precision Recall Curve for a classifier I built using AutoML. Most of the Precisio-Recall curves tend to start from (0, 1) go towards (1,0). But mine is the opposite. But I ...
- 151
0
votes
0
answers
43
views
Can you get a very good AUC-ROC score despite predicting all rows to have the same probability?
On the test set of a binary classification problem, the p25, p50 and p75 of the predictions are very close to each other (e.g. 0.123).
Is it possible that my model can achieve a high AUC-ROC (e.g. 0....
- 1
1
vote
1
answer
48
views
Does synthetic data be over sampled as well?
I'm building a binary text classifier, the ratio between the positives and negatives is 1:100 (100 / 10000).
By using back translation as an augmentation, I was able to get 400 more positives.
Then I ...
0
votes
1
answer
117
views
Imbalanced data: understanding example from Bishop PRML book?
I'm trying to understand the 3-step procedure to compensate for the effects of imbalanced data described in Section 1.5.4 - pg 45 of Bishop's PRML book. Please refer to the following excerpt from the ...
- 3
1
vote
1
answer
38
views
Will a classifier trained on undersampled data make accurate predictions on new imbalanced data?
I have a dataset with about 200,000 entries. The target variable is binary, and only 4,000 instances belong to the class of interest.
I would like to undersample the majority class so that we have a ...
- 85
1
vote
1
answer
31
views
Remedie for a stubborn recall result?
I was working on a project connected to predicting default on credit loan with 0-1 loss. The recall is a crucial measure that should be maximized in this case, while monitoring precision for sanity of ...
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
2
votes
2
answers
1k
views
Why does class_weight usually outperform SMOTE?
I'm trying to figure out what exactly class_weight from sklearn does.
When working with imbalanced datasets, I'm always using ...
- 191
0
votes
2
answers
28
views
Rough ideas of expected performance boost from over-sampling techniques?
I am trying to train a classifier for a multi class classification task. However, the dataset is very imbalanced. About half of the around 160 unique labels are such that there are only 10 or less ...
- 103
0
votes
2
answers
475
views
How to tell if downsampling helped model performance
Fitting a logistic classifier to imbalanced data. My target variable is 5% 1, 95% 0. As such, I think it's probably best to use the PR-AUC to evaluate the model rather than ROC-AUC. I get a PR-AUC of ...
