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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11 views

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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40 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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19 views

Class predictions at a given specificity - repeated cross validation

I have a small dataset (n~120, 22 of class 0). On this dataset, I am training a number of neural net models using different predictors, and want to compare their performance. Also, I am interested in ...
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53 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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49 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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21 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 ?
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13 views

Why my binary classification neural network performance oscillates a lot through epochs? [duplicate]

I am training a CNN with Keras, vgg16-like model and i don't understand the results. For example, in epoch 15 i have good results but in 14 and 16 it's horrible (you can see it in the loss). What ...
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22 views

class_weight = 'balanced' if GridSearch on unbalanced data set?

I'm trying to optimize the hyperparameters of an SVM. I have an unbalanced data set with more than two classes. In some classes very many samples are included in others very few. Using GridSearchCV, I ...
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25 views

Problems with SMOTE optimizing function

I am new to machine learning or R and tried to code a function "smotevalue" in R in order to fine-tune the parameters of SMOTE for binary classification/prediction in imbalanced data. The idea is to ...
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2answers
255 views

Sampling highly imbalance multi-class response variable

I have a dataset (11000 x 117) with response variable having multiple classes. Here is a plot of class distribution: Some of the classes have only 1 sample in the entire dataset and some have 2, 3 ...
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34 views

Multi-step ahead binary classification of multiple multivariate short time series

I'm working on a project where I need to identify loan defaults. I have around 50 000 time series, each time series represents a loan and is composed by few time steps (from 3 to 18). Each time step ...
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89 views

Can I use balanced subsample for training and imbalanced for testing?

I am working on classification problem and I have highly imbalanced, but huge data set (I have more than 2mio samples). Now my question is: If I choose subsample of only 15% of the data for training, ...
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49 views

Dealing with imbalanced dataset without using Undersampling or Oversampling

Let's say I have a dataset with 100,000 class A training observations and 400 class B training observations. I want to use Support vector machine for this binary classification problem. Instead of ...
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63 views

How to improve specificity with unbalanced data? (R caret package)

I am working on a classification problem where my outcome variable is either "Approved" or "Denied". The % of approvals in my dataset is roughly 60% and the denials make up roughly 30%. I have tried ...
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187 views

Binary Classification in Imbalanced Data; Oversampling and Imputation

Together with two friends I participate in a university course about data mining in R and we chose the topic of bankruptcy prediction. We started with some "clean" data found on an "In class" kaggle ...
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62 views

Difference between BalancedRandomForestClassifier and sklearn.ensemble.RandomForest + RandomUnderSampling [closed]

What is the difference between fitting training data with imblearn.BalancedRandomForestClassifier compared to using sklearn.ensemble.RandomForestClassifier + imblearn.under_sampling.RandomUnderSampler ...
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214 views

Calibration curve of XGBoost for binary classification

I'm working on a binary classification problem, with imbalanced classes (10:1). Since for binary classification, the objective function of XGBoost is ...
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32 views

My intercept is negative for a logit prob regression and I can't interpret

This is my first time building a model outside of school. I cleaned the data and ran Cohen's Kappa and cutoffs/ROC as well as did random forest. The accuracy of predicting the 1 outcome is about 37% ...
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55 views

Bad results in a Loan Default Prediction Problem

I have a dataset consisting of 23 features for a number of clients : Client ID yearly financial ratios couple of qualitative features and a binary default variable I'm trying to create a model that ...
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31 views

Running SMOTE on unbalanced data

I've read a few answers to similar questions that advise the completion of SMOTE after splitting the data set into Train and Test sets however, the documentation and other examples I've seen run SMOTE ...
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28 views

Which classification algorithms are negatively affected by class imbalances?

I've seen a few posts and papers floating around the web (mostly those related to over/undersampling, SMOTE, and cost-sensitive training) that, when discussing class imbalance, specify that certain ...
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16 views

Class Imbalence Problem even after Balancing Data

So I am training a neural network on a binary classification problem and my Case (1) and Controls (0) were imbalanced so I oversampled my cases so that that the training set was 0.5053 made up of ...
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35 views

Is it normal to have big difference between train loss and test loss on neural networks when using class weights

I am training a simple feed forward neural network in Keras, to perform binary classification. Dataset is unbalanced, with 10% of class 0 and 90% of class 1, so I was adding a class_weight parameter ...
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15 views

Boosting models on nested variables & imbalanced columns data frame

My data frame is defined by a structure such as below : The mainQ1 is the question stating do you have A,B,C,D,E,F and is the top of hierarchy with binary outcome ( 'Y' or 'N'). If it is answered 'Y'...
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Unbalanced classification problem [duplicate]

I am trying to build models for the KDD cup 2004 challenge. The protein homology problem data is divided into several blocks with roughly 1000 samples in it. Each block has an unbalanced class problem....
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40 views

two sample analysis with outliers

I've got some data from two groups which have different sample size. (univariate variable like 'price', and want to test whether they have significant difference ) The sample size of 'A' is 10,000 ...
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23 views

Resampling methods for curves and time series

In the case of imbalanced datasets, different oversampling/downsampling methods exist such as SMOTE, ADASYN, etc. However, this methods mostly simply interpolate in the feature space, treating the ...
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20 views

How does undersampling help classification of imbalanced data set?

In a 2 class classification and a dataset with majority Class 0 and minority Class 1, undersampling of the majority class (Class 0) is sometimes used to aid in classifying the minority class (Class 1)....
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14 views

How to deal with training data after cross validation?

I have imbalanced data and used undersampling to construct several logistic regression models, using the way very similar to EasyEnsemble. The parameters, like regularization, number of models were ...
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309 views

Difference between class_weight and scale_pos_weight in LightGBM

I have a very imbalanced dataset with the ratio of the positive samples to the negative samples being 1:496. The scoring metric is the f1 score,and my desired model is LightGBM. I am using the sklearn ...
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16 views

non linear mixed effect model with unbalanced data

I am using nlme to model the growth curves of individuals that are in 4 different groups, using R. The number of individuals in each group is completely unbalanced (n1=344, n2=51, n3=34, n4=25). ...
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42 views

Random oversampling versus classes weighting for class-imbalanced dataset

I want to train a multi-class classification deep learning model. But my dataset is class-imbalanced. So considering 2 solutions, random oversampling and classes weighting, I have some questions: Do ...
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12 views

Accuracy and F-mesure for imbalanced datasets

I have 10 imbalanced datasets. Classes are : 1, 2, 3, ..., 10,11,12. I used as evaluation metrics for my datasets accuracy and F-measure. The F-messure of each class in each dataset is as below: Is ...
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14 views

Fixed Effects Panel Data

I am trying to run a fixed effects regression but unfortunately having some issues. I hope someone could help me. I want to regress $P_{itc} = \mu_{c} + \phi_{b} + \gamma_{s} + \sigma_{1}*tax_{i,t} ...
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15 views

unbalanced panel data: is it possible to use?

I am trying to analyze unbalanced panel data. Honestly, it is first time to do longitudinal study, so I have many difficulties. I really need your help. I consider to use GEE or RE (in STATA) ...
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38 views

Choosing error function for regression

I have a dataset with ~100K samples and non-negative continuous target variable. 99% of target values are zeros and the remaining 1% are right-skewed. Here are the deciles (0 and 1 correspond to min ...
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1answer
50 views

Train/Test split for imbalanced regression problem

I have a dataset with ~100K samples and continuous target variable which has 95% of zero values. Since there are high-dimensional categorical features and missing values in my data, I plan to use ...
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21 views

The coefficients and p-values in the Firth logistic regression when the data set is imbalance

My question is that in the case where the contingency table has imbalance data in terms of binary response success and failure, can I confidently say that the two-level categorical predictor is ...
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19 views

Interpreting results of a class-balanced model?

I'm working on a logistic regression model in order to model a relationship and am facing a class-imbalance problem (way too many 0's and not enough 1's). In order to resolve this, I'm planning to use ...
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69 views

Is Stratified K Fold CV Needed when Estimator implements Balanced Class Weight?

I am working on a classification task with an imbalanced dataset. I am using Sklearn's ensemble RandomForestClassifier and set its class weight to Balanced. My question is, when I then GridSearch it, ...
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97 views

Can balanced accuracy be higher than accuracy?

I have classification tree where the balanced accuracy of the test set is higher than the normal accuracy. I thought balanced accuracy can only have at his maximum the same value as the accuracy not ...
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1answer
43 views

Practical interpretation of Precision-Recall AUC

I have a classifier with an AUC (PR) of 0.06 which I will use for a practical interpretation. My test set consists of three months of data with a total of 2,200,000 observations of which 0.03 are ...
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23 views

How can the balanced accurcay be bigger than the normal accuracy in unbalanced test data? [duplicate]

I constructed two binary classification tree's on two different training set's that i balanced with oversampling and undersampling. The test set is still unbalanced. After that i computetd the ...
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35 views

Improveness given a certain AUPRC

I am training a machine learning model (Random Forest) for a multiclass problem (64 classes) in which most of them are highly imbalanced. That's why I am using mainly F1 score for checking the model's ...
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1answer
70 views

Performance Imbalance Dataset Decision Tree

I have a imbalance dataset for a classification task, with the minority class accounting for about 21% of the total. When I use a decision tree based model for prediction, let's say a classification ...
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22 views

Filter-based feature selection for binary classification with unbalanced classes

I have a data set with ~10k observations and ~50 features. Each observation is assigned one of two classes (labeled 0 and 1, say). Approximately 98% of the observations are class 0, and the remaining ...
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22 views

Do ANOVA & MANOVA require balanced levels?

I have one independent variable with two levels, or categories, and four dependent variables. When doing a MANOVA or ANOVA test, how important is it for the number of observations in each category to ...
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47 views

Binary classification for imbalanced distribution of target/response class for age

I'm trying to build/train model that depends on many attributes where age is the most important one (it has significant impact on AUC). Overall target class count is quite balanced (+40% vs. -60%) ...
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Predicting user behaviour based on transactional data - flagging “risky” behaviour

Firstly, I'm not sure if this is the right instance of StackOverflow to post on so feel free to ask me to put it elsewhere. I am exploring the concepts of clustering and "unsupervised" learning for ...