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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7 views

Discrimination vs calibration

So far I have been using logistic regression for binary classification problems usually for unbalanced classes - and would resort to the standard F1 score, AUROC and Gini to compare and contrast the ...
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metric for unbalanced subsets in an active learning scenario

I am looking for a metric which measure "How unbalanced" a data subset is BUT since I will use it in an active learning scenario which means that my subset might not only unbalanced but also might not ...
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39 views

Why do you need a balanced test set?

It seems to be the consensus that, if possible, both train and test set for binary classification should be balanced over the two classes, especially if using classifiers like SVM. Whilst I ...
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42 views

Normalize output scores for binary classification

I am handling a binary classification problem on an imbalanced dataset. The goal is to create a system able to insert the returned score (probability to be in the positive class) in a bins between 1 ...
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36 views

Perform ANOVA on imbalanced Data in Python

Is there a way I can perform ANOVA [or any statistical test] for the following research problem. I have a variable Plant Survives (1/0) and another variable Amount of Hydrogen( Continous). I want to ...
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Do imbalanced categorical predictors do any harm when classifying?

Assume I want to do some, say, churn analysis on a dataset. Decision Trees, for instance, are relatively robust to skewed distributions in the (numerical) features, but rather poor on imbalanced ...
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15 views

Low recall when positive is the minority class?

I have 2 versions of the same dataset, one which is fully balanced and one in which the positives:negatives is 1:2. In both cases, when I train my SVM classifier I get low recall and quite high ...
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Does `friedman.test()` need assumption of `balance design`?

I want to know whether friedman.test() need assumption of balance design. Then I searched R ...
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Neural Networks: How to set the weights for weighted sampling for semantic segmentation?

I'm currently trying to do semantic segmentation with a deep learning model on images. The dataset is highly imbalanced and i would like to try weighted sampling. I'm using pytorch and a dataloader ...
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Steps for a highly imbalanced classification steps. Should I up-sample & under-sample data or just up-sample the imbalanced class

I have a highly imbalanced binary (yes/no) classification dataset. The dataset currently has appx 0.008% 'yes'. I need to balance the dataset using SMOTE. I came across 2 method to deal with the ...
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sklearn Logistic Regression Sample Weights & Duplicate Samples

Can you achieve the same model w/ two different, but arguably same datasets: Dataset A: Duplicate samples w/o sample weights Dataset B: No duplicates w/ sample weights where the sample weights equal ...
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Naive Bayes : intuition that resampling (down/up/SMOTE/ROSE) affects prior probabilities in a wrong way

I have a supervised classification problem with unbalanced class to predict (Event = 1/100 Non Event). I have the intuition that using resampling methods such as down/up/SMOTE/ROSE with Naive Bayes ...
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What is a good PR-AUC and should I undersample time series for rare event detection? [duplicate]

I have a binary classifier for a highly imbalanced multivariate time series. I use an LSTM Network to predict the next time step and use the prediction error to decide whether a data point is an ...
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Simulating unbalanced data for GLMM in R

for a power analysis I am simulating some multilevel data for a random-intercept linear model. I found that simulating perfectly balanced data, with the same number of observations between the groups ...
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As to unbalanced design sample data,do we need to check variance homogeneity as precondition of two way anova?

For the balanced design data,I know variance homogeneity among each cell is the requirement of two way anova. As to unbalanced design data,do we need to check the variance homogeneity of each cell? If ...
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Feature Engineering: How to deal with imbalanced numerical/categorical features

I'm analyzing a data set and solving a classification problem and find that values concentrate on one number in many features. For example, a categorical feature 'loan' indicating a person having loan ...
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18 views

Maximize F1 Score for an imbalanced data and multi-class classification

I'm dealing with an multiclass classification problem. The data is textual and too imbalanced. I see that the models that i'm building using the character level or word level grams are always giving ...
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Non-parametric confidence intervals for multiclass, unbalanced dataset

I am trying to get non-parametric confidence intervals (I'm using this as a reference) over the classification metrics for a dataset. I have a few questions over the procedure. As I gather, once I ...
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33 views

Which performance metrics for highly imbalanced multiclass dataset?

I have a dataset with 5 classes. About 98% of the dataset belong to class 5. Classes 1-4 share equally about 2% of the dataset. However, it is highly important, that classes 1-4 are correctly ...
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Selecting training images for object verification(siamese network), different number of examples per object

I'm trying to build a model for object verification (my first not tutorial-guided project of this kind). I saw an approach using a siamese network in the coursera deep learning course by Andrew Ng. ...
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29 views

Multi-classification: low precision due to imbalanced classes in test data - what to do?

I built a multi-classification model with 3 result classes (XGBoost using R's caret-package): A, B and C. I undersampled my training data - so every class is equally abundant for training. The ...
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25 views

Is better to use a multiclass classifier or a set of binary classifiers?

I have to build a general method to perform multiclass classification. The number of class in the target variable is not fixed (it is probably in a range between 3 and 10). I would like to know if it ...
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STATA: Year fixed effects unbalanced panel data

I am studying fund performance and trying to regress funds performance on the performance of previous funds by the same PE firm. The data I have is basically panel data, but it is highly unbalanced (...
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geometric mean for binary classification doesn't use sensitivity of each class

scikit-learn's contrib package, imbalanced-learn, has a function, geometric_mean_score(), ...
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Sci-kit Learn Beta Score interpretation: How to use the 'beta' and 'average' param correctly?

I am building a model for a class imbalance problem, which I want to be as recall oriented as possible for the minority class. The model I have built uses class weights to penalize the majority class (...
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32 views

Imbalanced data for multiclass classification with ConvNet

I am trying to apply the SMOTE sampling technique to over-sample the minority class of a multiclass (5-class) problem using the convolutional neural network. As far CNN requirement, the input shape ...
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25 views

When working with unbalanced data, do we train final model on full data set?

Let's say we have an unbalanced data set. We randomly sample an amount from our larger class so that we have a balanced data set. After tuning parameters/hyperparameters and determining which ...
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Real life class imbalance [duplicate]

Fellow like-minded people, I'm writing my thesis in fake news detection on scrapped twitter data and facing an issue (among many others). Fake news consist of less than 10% of the total tweets or ...
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30 views

How R randomforest sampsize works?

I am working on a predictive model (imbalanced data) and trying to undersample the majority class data. I wanted to get the representative sample of my majority class and somehow came to know about R'...
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15 views

unbalanced three way ANOVA with missing data from one level of a factor

I wanted to make sure I was doing everything right, I believe I am supposed to do a three-way ANOVA but correct me if there is a better way to go about it. I have data on the feeding retention of ...
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1answer
20 views

Should I upsample both my training as my test set?

I have a highly unbalanced dataset (1000 vs 60). Where I want to use upsampling. The real life distribution of the problem (predicting no show) is probably also very highly imbalanced. My question is ...
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13 views

Classifiers evaluation

I'm building a classifier which will detect failures in production of lithium-ion batteries. The classifier has to detect failures with >=90% precision. To do that, I'm building different binary ...
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29 views

What are benchmarks for precision when working with unbalanced data?

I have a dataset where the positive class is 1.7%, which equates to about 40k positive cases and a total basis of approx 2.5m. What is a realistic precision to achieve for the most likely to cancel? ...
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What is the affect of datasets having (Events Per Variable) EPV less than 10?

I read some studies in Software defect predictions (published in good journals) that mentioned that we should use datasets with an Events Per Variable (EPV) greater than 10; otherwise the results will ...
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Does My Custom Class Imbalance Handling Make Sense?

So I have a data set of size 250k, and my minority class is of size 5000. This is a pretty imbalanced dataset. I did not apply sampling in my model, and it turns out when split it into train, test, ...
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51 views

How to modify Presicion and recall of GLM (Logit) in R? [closed]

I fit a logistic regression model with an unbalanced population in R. The problem that I am getting is I have 0.4 for precision and 0.0018 for recall, so I want to modify the threshold in order to ...
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70 views

Improve F1-score for multiclass text classification with highly imbalanced dataset

I am trying to classify clients' complaints with a dataset of 180k complaints. I have 132 classes like this: Counter({'DIAG_000_NODIAG': 66291, 'FORWARD': 29126, 'DIAG_087': 22843, 'DIAG_049': 17668, ...
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1answer
80 views

Are mixed model results valid when several missing replicates?

I want to know if it is correct to take as valid the results of a mixed model (lme) test for a triple factor experiment with several missing replicates in only one level factor situation. My ...
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88 views

Why decision tree handle unbalanced data well?

one approach to deal with the unbalanced dataset is to choose the models that can hand this type of dataset well such as decision tree, but why decision tree can handle the unbalanced dataset well?
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19 views

Balanced Classes Performing Worse than Imbalance Classes

I am training a model on a data set that has imbalanced classes; 97% of the labels are 0 and 3% are 1s. I chose to upsample the data in order to make the classes equal in the model training. When I ...
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21 views

Performance metric for continuous binary classification method

I have and imbalanced data set with two classes of data: $A$ and $B$. I apply a method that assigns a continuous probability to each element of belonging to class $A$: $P_{A}$ , where $P_B=1-P_A$. I ...
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15 views

Improving accuracy of multi-class text classifier

I am trying to build a text classifier with 4 highly imbalanced classes. Data has around 4000 documents and highly sparse. I have used XGboost and few other algorithms.Highest accuracy is given by ...
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73 views

Binary Classification Propensity Scoring: High Accuracy in Train/Validation/Test, but Low Accuracy on Production Data

Before I describe my problem, if anyone has material for propensity scoring I would love it if you posted some. I've done a lot of research on propensity, but I think I've bled myself dry on the net. ...
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34 views

Imbalanced dataset - Majority positive class

My dataset consists of 150 patients where 50 are controls/healthy (negative) and 100 are sick (positive). If I want my model to have high sensitivity at high specificity (left side of the ROC), in ...
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2answers
137 views

How to approach unbalanced data with unequal sample sizes for comparing means

I have a data with a continuous and two categorical (population and sex) variables. I want to test whether the means among the groups are significantly different. However, this is not an experimental ...
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115 views

SMOTE for multi-label classification

I have a dataset with 77 different labels. Each sample has one or more of these labels. I did some data analysis and found out that the dataset is highly imbalanced - there are a large number of ...
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1answer
65 views

Base rate of accuracy after resampling for classification problems

If I had an imbalanced dataset with 10% positive instances and 90% negative ones, the base rate for accuracy before resampling is 90%. But what about I resampled the data such that I have an equal ...
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Scaling baseline and SHAPs back to original class rate

I have an imbalanced dataset (positive class rate = 1%) and have downsampled the negative class to give me a 50/50 balance in the two classes. The outputs from this model look adequate. Ignoring the ...
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39 views

Classification models:Overfitting due to sampling issue

New to ML,I have used smote/sampsize for the first time, so sorry if the questions are very basic.I have a dataset with a factor response variable ("Y" , "N" )in the ratio(Y:N=3:7)(classification with ...
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35 views

Unbalanced data set - how to optimize hyperparams via grid search?

I would like to optimize the hyperparameters C and Gamma of an SVC (SVM scikit-learn) by using grid search for an unbalanced data set. So far I have used class_weights='balanced' and selected the best ...

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