Questions tagged [unbalanced-classes]

Data organized into discrete categories or *classes* may present problems for certain analyses if the number of observations ($n$) belonging to each class is not constant across classes. Classes with unequal $n$ are *unbalanced*.

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Problem of unbalanced data

unbalanced data is an issue that can effect the performnce of classification model ,several remides can be done to balance the data two of them are upsampling and downsampling , my questions is : how ...
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Will oversampling help with generalization (small imbalanced dataset)?

I have an imbalanced dataset (2:1 ratio) with about 60 patients and 80 features. I performed RFE + stratified cross validation to reduce the features to 15 and I get an AUC of 0.9 with Logistic ...
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How to improve Recall and Precision?

I am working on a big data set which has 25 features with 237862 number of rows. I am trying to predict return . 1 is for return and 0 for no return. My data set has 12% of data which returned. So ...
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15 views

ML Models for Speaker Identification

I am working on speaker identification problem using GMM (Gaussian Mixture Model). I have to just identify one user present in the given audio, as GMM model output log probability how to set the ...
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12 views

Gains on test data set higher than that on training data set post balancing

I have an imbalanced data set (96-4 split between No and Yes cases). I am trying to build a decision tree model for classification after balancing my data set(tried different thresholds for ...
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22 views

How to perform Repeated Measures MANOVA, with unbalanced design [closed]

I am analysing data from a vaccination study and I think that the best way to model my data is with a repeated measures multivariate ANOVA. Because I have different #'s of subjects in each study ...
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How to handle a feature (x1) with huge sample space for linear regression ? (n(x1) >> n(x2,x3,…))

I have a regression problem to solve, comprising of approx 2000 data points. I have some 7-8 features, but one of the feature has a huge sample space, meaning When I one hot encode this Feature, it ...
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8 views

Regression/anova with imbalanced controls

I am thinking of a regression setup where the dependent variable, Y, is continuous and the predictor is a factor. The data is an aggregation of multiple sources with similar and pooled controls but ...
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15 views

Interpreting results of resemblance-based permutation methods: PERMANOVA and PERMDISP?

I am working with an invertebrate data set (i.e., counts of individuals per invertebrate order, captured by pitfall trap) and am exploring trends in community composition in relation to environmental ...
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35 views

imbalanced dataset - class weight vs weighted loss function

I'm working on a classification problem with a very imbalanced dataset. Many papers mention a "weighted cross-entropy loss function" or "focal loss with balancing weights". I can't find any of those ...
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23 views

Deal with imbalanced data [duplicate]

Building machine learning models to do forecast, sometimes the dataset was used is imbalanced, and there are some methods to deal with this issue such as the resample method and choose other metrics(...
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24 views

Binary Classification problem for imbalanced dataset

I am new to machine learning and need help. I have a dataset with two classes(0,1) where is 0 is Profitable and 1 is Unprofitable. Ratio of 0:1 in train set is 150/52 Taking positive as "1"(...
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Running SMOTE on very large class imbalanced datasets - batched or subsampled implementations

There is a theoretical and computational aspect to this question. I was trying to use SMOTE to reduce class imbalance in a rather large dataset--about 8 million rows. The data has a binary outcome ...
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Unbalanced data with logistic regression: good references?

I am using the logistic regression framework to formulate a classification model. I have a dataset with 42 'true' (response variable) values and 4400 'false' ones. By using the ‘rule-of-10’ and other ...
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9 views

Test and Validation datasets for imbalance classification tasks using SMOTE or other over/under methods

I was reading up more on class imbalance and over/under-sampling methods to help reduce the imbalance and improve ROC. I am working on an extremely imbalanced dataset for click-prediction, so like ...
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Class imbalance, Random Over/Undersampling and the use of Class Weights and bias

I was looking at the Tensorflow tutorial on "Classification on Imbalanced Data". Now they first show how to apply some approaches to deal with imbalance using model weights and a pre-initialized bias ...
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23 views

Oversampling/Undersampling in respect to Train and Test - Isolation Forest

I've got a quite imbalanced data set. 144.496 : 162 -> ratio of 1000:1 I would like to use IsolationForest to detect the 162 anomalys. I've already split the data. However, the iForest doesn't ...
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penalized logistic regression for un-balanced classes--why does it make sense to keep all the zeroes?

So I have been looking at some public datasets on fraud detection, where the dataset contains a 0/1 column for whether the transaction is fraudulent and then a large set of numerical and categorical ...
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getting low precision for deep neural netwok [duplicate]

iam working on a deep neural network (DNN) model to classifie object into two classes ( 0 , 1) , iam using keras api to build and train the model architecture i build below: ...
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Chi-square Test with High Sample Size and Unbalanced Data

I have a data set which has high values. I want to make a chi-square test on this set. +--------+---------+---------+---------+---------+ +----------+ | 15-19 | 20-24 | 25-29 ...
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Why does a class weight fraction improve precision compared to undersampling 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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logic behind balancing? [duplicate]

I am a newbie in stats, and while reading: https://towardsdatascience.com/having-an-imbalanced-dataset-here-is-how-you-can-solve-it-1640568947eb I don't seem to understand why is an imbalanced ...
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34 views

Imbalanced data classification with GLM giving very poor results [duplicate]

I have a loan defaulters dataset and it is highly imbalanced as shown below: 0 1 33108 673 I have tried SMOTE to balance the dataset, as shown below: ...
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Maximum level of label noise for binary classification so that dataset is “Learnable”?

Assume we have an imbalanced dataset (minority label frequency 1-20%), where subset of samples have their labels randomly flipped. Now, of all samples with positive label (the minority class) in this ...
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Zero inflated and hurdle models - is it common do 'build your own' with e.g. ensemble model?

I have been given a new analytics problem to solve. The context is app analytics where we would like to predict total revenue per app install after 30 days from install based on just 7 days of data. I....
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How much is the Class Imbalance Problem rates?

I'm working on a data set and wanted to know is there a standard rate about Class Imbalance problem or not? I have 47 samples in Class A and 150 Sample in class B , should I use Class Imbalance ...
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Imbalance class data resample gets results in overfitting Random Forest

I am working with a very imbalanced dataset (16k lines, 4% in the minority class), using random forest to for a binary classification. I’m using the Python Sklearn implementation of ...
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23 views

After applying SMOTE, the class distribution doesn't match the real world. Is this a problem? [duplicate]

I have an extremely unbalanced dataset with two classes: 1: 1,800 # class 1 0: 40,000 # class 0 This is real world customer data of churned/not churned If I ...
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When is oversampling preferable to undersampling and vice versa?

When data is unbalanced, that is, when the distribution of classes being predicted is very uneven (e.g. 90%/10% for two classes or 10%/15%/75% for three classes), many machine learning models have ...
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25 views

How to test for a statistically significant difference between multiple unbalanced groups

I have 4 groups that are not normally distributed. I would like to know if they are significantly different, with the aim of knowing if they would make a good feature to base a classification on. I'...
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Any papers or unpublished work on relationship of downsample/upweighting and importance sampling in the context of training imbalanced classes

I understood that the general Q of training highly imbalanced data has been asked and answered many times. I have skimmed through the other threads and felt fairly confident my specific question/point ...
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Does Calibrating The Model Affect Its Prediction Capabilities

Suppose, I have an imbalanced training set and train a model on it, it will be biased towards the majority class but the probability estimates would be much better calibrated as it would follow the ...
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Finding variation in mean of some parameter for different groups

Let's say I have some data on students' test scores that consists of id, category1, category2...
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26 views

Should I use stratify parameter for scikit-learn train_test_split while dealing with highly unbalanced dataset?

I have a dataset with over 200000 records. Only 400 of are positive, which makes the data highly unbalanced. I cannot collect more data. At first I trained a decision tree. I used StratifiedKFold and ...
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24 views

Precision-Recall Curve for imbalanced dataset and effect of swapping positives and negatives

We are currently trying to evaluate the performance of a binary prediction model, on a dataset which has a majority of positive samples. Having done some research, we read from this paper that pr-auc ...
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19 views

How does Data Augmentation work for supervised learning models?

I've ran into a few Kaggle competitions where the winning solution used data augmentation, and a new ML platform, which claimed to help with Data Augmentation. Use cases were imbalanced classes and ...
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interpretation of precision and recall when oversampling or undersampling in mlr

I balance my dataset with e.g. cpoUndersample() from mlrCPO Does this balance my test-set as well? This is important because ...
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29 views

In context of neural network training for regression task, Should the continuous label be of uniform distribution?

Currently, I am using Deep Neural Network (DNN) for the regression task. The training data is time series and contains 7 input features and 836 training instances (samples). Here the label is ...
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53 views

SMOTE in unbalanced dataset with binary features

after reading different posts about unbalanced datasets I didn't make my mind clear about my specific problem so that's why I'm posting a new question. In my case, I have a dataset with around 20K ...
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How to quantify performance of subclasses?

I have a dataset of $N$ points. Each point $p_n$ has an associated label $l_n$ which is either $0$ or $1$, $n=1$ to $N$. Let $\overline{l}$ be the vector of all $l_n$ stacked together. Say every point ...
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Why Log loss, AUC and precision & recall change differently when class imbalance problem is tackled?

I have a dataset and I'm working on a binary classification task with it. It has a class imbalance problem where False class versus ...
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12 views

Random sampling for both under- and over-representative class

I have an unbalanced dataset. Let's say I have 500 positives and 50,000 negatives. Can I deal with this by randomly choose 300 out of 500 positives and also 300 out of 50,000 negatives? Does this ...
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45 views

Oversampling methods for numerical data (regression)

There are many oversampling methods for categorical labels (for example SMOTE and Rose, etc.). But, are there oversampling method for numerical labels (the thing that I want to predict with my ...
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Is doing oversampling on train set and undersampling on test set correct?

I have an imbalanced dataset (95% in class 0 and 5% in class 1) and I am using machine learning for classification. The AUC(Area under ROC curve) was high (about 0.86) but AUPRC(Area under precision-...
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Do effects usually reproduce on larger NN architectures?

While reading this paper on class imbalance I couldn't help but notice they were using a small network - a Resnet-10 - to check what class imbalance, ...
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65 views

performance measure suited for imbalanced classes and robust towards changing class ratios

I am looking for the best performance measure. My use case: I want to find out which dataset can be modelled best with binary classification. The datasets have an active minority class I am ...
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3answers
278 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 ...
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51 views

Business Productivity: How do I measure workflow changes' effects on number of work items completed?

I am designing an experiment to measure the effects of two different workflow modifications, individually or combined, on overall group productivity. The null hypothesis is that the modifications ...
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GLMM - how unbalanced is unbalanced, how under-dispersed is under-dispersed?

I have a large data set, with vegetation data sampled at plots that are Sand or Clay soil type at 20 sites. I plan to fit a GLMM to the data, with 'soil type' as both a fixed effect and as a random ...
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29 views

Evaluate imbalanced classification model on balanced sample

Why would be too optimistic to compute presicion, recall and f1-score to evaluate a model trained for imbalanced classification on a balanced testing sample ?