All Questions
Tagged with class-imbalance classification
51 questions with no upvoted or accepted answers
3
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0
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806
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Target mean encoding worse than ordinal encoding with GBDT ( XGBoost, CatBoost )
I have a dataset of 23k rows of an unbalanced dataset 85/15 ratio, 10 variables ( 9 of which are categorical ) , i'm using CatBoost and XGBoost for a binary classification.
I applied cv (5 iteration ...
- 2,094
3
votes
0
answers
436
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Adjust class weights due to class imbalance and class importance Multi class classification XGBoost
With respect to this question and the answer given by @Esmailian, Would anyone be able to let me know if Class B has a higher importance or the positive class ( i.e. it needs to have a higher ...
- 153
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 ...
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2
votes
0
answers
2k
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Binary classification with imbalanced dataset, about lightgbm output probability distribution
I trained a binary classifier for an imbalanced dataset. I did two experiments:
lightgbm classifier, boosting_type='gbdt', objective='cross_entropy', SMOTE upsample
After training the lgbm model, I ...
- 21
2
votes
0
answers
185
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cost sensitive loss function in lightbm with individual cost
i am looking for a cost sensitive function that will have weights according to individual row feature (like amount)
this way i can penalize more FN which has large amount vs. low dollar amount.
took ...
- 121
2
votes
1
answer
1k
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Generate a balanced batch with ImageDataGenerator() and flow_from_directory()
Hi I am new to python and deep learning. I am doing a multiclass classification. My 3-classes dataset is imbalanced, the classes take about 50%, 40%, and 20%. I am trying to generate mini batches with ...
2
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0
answers
1k
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Data Augmentation techniques for classification of imbalanced time series datasets
Now I have a task to classify the imbalanced time series datasets using ML classifiers, such as Logistic Regression, Decision Tree, SVM, and KNN. I am not allowed to use the Deep Learning tools, such ...
- 21
2
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0
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415
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multi class classification : unbalanced data - good testing results poor prediction results
I have unbalanced dataset with 11 classes where 1 one class is 30% and rest are between 5-12%. I am not a hardcore programmer so I am using the product from https://www.h2o.ai/.
I used GBM and DRF ...
- 41
2
votes
0
answers
86
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Classifying Sequences Where Some Sequences in Both Classes
I am building a modified RNN (specifically a GRU) to classify sequences. These sequences are of variable length, and contain "states". Each point in the sequence is categorical and they look like [A,B,...
- 21
1
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0
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19
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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
1
vote
1
answer
48
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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 ...
1
vote
1
answer
38
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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
3
answers
490
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How do I deal with unbalance classes in a stock market prediction problem?
I am working on a prediction model to predict whether a stock should sell, hold or buy in n days. Each day (or row in the dataset), I classify whether this should ...
- 111
1
vote
1
answer
28
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Many questions training unbalanced and duplicated data
I'm a DS student. I have like 30.000 of bank statements, all labeled with a specific category(cat1, cat2, ...). With that data I'm trying to train a classification model but I found several problems:
...
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1
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0
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53
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calibrating classifier probabilities for unbalanced data when class ratios are unknown
I've built a binary classification convolutional neutral network, trained on simulated data with equal numbers of simulations for each class. I've obtained good results for a validation set with equal ...
- 111
1
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0
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31
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How do I build a model to improve CTR on campaign?
I am trying to build a propensity model for a client to increase the CTR.
Client has the list of people who clicked in the previous campaigns but doesn't have the data on the list of people who didn't ...
- 111
1
vote
0
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333
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g-mean for binary classification doesn't use sensitivity of each class?
scikit-learn's contrib package, imbalanced-learn, has a function, geometric_mean_score(), ...
- 111
1
vote
0
answers
46
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Multiclass Classification Task - Performance on Each Class Compared to Chance?
As a part of a classification task, a classifier has decided whether different books belong to class A, B or C (which are imbalanced) by looking at certain feature of the book.
I have calculated ...
- 19
1
vote
0
answers
334
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Best strategy to build Classifier for Mixed Data with class imbalance
I have a dataset which contains : 94 numeric features + 56 categorical features
I am trying to build a classifier to predict Target (disease/healthy).
2. Rows : 1812
3. Class imbalance ( Majority ...
1
vote
0
answers
38
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Best approach for classification problem where examples most belong to one set
I'm working to build an opt-out filter for my company. I have a small amount of machine learning experience (I've done a few projects with tensorflow in the past), but wanted to get other opinions on ...
- 213
1
vote
0
answers
136
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Kappa Goes up as Accuracy Goes Down
I have recently been trying to train a randomForest model on a binary outcome with a very uneven class split.
282 control ~82%
63 case ~18%
There are a total of 147 predictors that I'm testing for ...
- 11
1
vote
0
answers
27
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SmoteBoost: Should SMOTE be ran individually for each iteration/tree in the boosting?
As per the paper on SmoteBoost, SMOTE is ran for each iteration of the boosting, generating N samples, which are further added to the original training data and the weight distribution of the ...
- 11
1
vote
0
answers
278
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Increase Specificity of a model using SMOTE arguments from DMwR package in R when training data is unbalanced
I'm working on a binary classification problem and training data which I'm using is unbalanced.
I used SMOTE function from DMwR ...
- 111
1
vote
0
answers
422
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Fraud detection using auto-encoders and Keras
I am following this example to learn a bit about the use of auto-encoders in fraud detection.
Now that I reached the end of the article, two questions rose in mind:
Can we train the network in an ...
- 547
1
vote
0
answers
667
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Which type of classification best suits this scatter plot?
The following image shows a scatter plot of my data. The Y axis points are the labels, labeled from 1 to 6 and X-axis are dimensionally reduced values of all my features. I reduced them for better ...
- 671
1
vote
1
answer
567
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Improving Performance of Machine Learning for A Small Imbalanced Dataset
I am a researcher in Machine Learning. In my project, I have been applying ML to a small imbalanced data consisting of 8 features and 297 instances with 44 positive instances and 253 negative ones. ...
- 86
1
vote
2
answers
789
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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 ...
- 755
1
vote
2
answers
290
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Preferred approaches for imbalanced data
I am building a binary classification model with imbalanced target variable (13% Class 1 vs 87% class 0). I am considering the following three options to handle the data imbalance
Option1: Create a ...
- 755
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
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
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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 ...
0
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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
2
answers
533
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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% ...
0
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0
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97
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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 ...
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0
votes
0
answers
40
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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 (...
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0
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0
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28
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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
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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
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0
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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
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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
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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 ...
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0
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0
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43
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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
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
0
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0
answers
468
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Steps for balancing data using SMOTE
Right now I'm doing sentiment analysis (classification) with TF-IDF and SVM linear. My data is not balanced and I want to make data balance using SMOTE from ...
- 101
0
votes
0
answers
514
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XGBoost failing on highly imbalanced data!
I am working on a classification problem, where I am trying to predict a fraud login. The data is highly imbalanced i.e.
0 = non fraud logins , 1 = fraud logins
0 : 4538076
1 : 365
I have been trying ...
- 1
0
votes
1
answer
152
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Data simulation using make_classification in Python
I have a question about data simulation in Python. I deal with the classification of imbalanced data and want to test the effectiveness of different methods on simulated data. I have seen in various ...
- 21
0
votes
0
answers
606
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Does resampling imbalanced data decrease the precision of a model?
I have a model with an imbalanced dataset, lets say 5% of the rows are from the positive class. If I resample my data using something like SMOTE, or removing rows from the larger class (downsampling), ...
0
votes
1
answer
39
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[under/over]-sampling teaches model the wrong distribution?
TLDR: Will under/oversampling during the training phase teach the model the wrong distribution and adversely affect accuracy?
Let us assume you want to train a classifier to differentiate between ...
0
votes
0
answers
137
views
Why does a class weight fraction improve precision compared to under-sampling approach where precision drops?
I have an imbalanced data where the ratio between positive to negative samples is 1:3 (positive samples are 3 times higher than negative). For my case it is is important to have a higher precision (...
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