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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23 views

Imbalanced class issue

I am taking my first steps in machine learning and data science area. I know for sure that my next task will be related to the imbalanced class problem. I’ve walked through many articles covering this ...
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Two datasets have different imbalanced class. How to split?

I'm new in machine learning. Actually I have two datasets files as my scrapping results from different news webpages. I want to preprocess to just selecting relevant news. So I would like to perform ...
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For imbalanced datasets, is it necessary to use undersampling or upsampling if one evaluates the performance of ML classifier using PR curves?

Previously, I would have assumed evaluating the classification performance of Decision tree and SVM with a PR curve would obviate the need for under/over-sampling since it doesn't evaluate the true ...
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Is there a name for this: dealing with class imbalance by learning to rank?

I've seen the following approach used in link prediction settings, and I think it exists in many other areas as well. Say we have a very imbalanced binary classification dataset, with imbalance in the ...
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Which classification metric to use and why? [duplicate]

I have a random forest classification model that classifies into 3 different labels. The thing is my data is unbalanced label 1 dominates the data with frequency of 0.5 while label 2 is around 0.35 ...
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Overfitting very quickly when using SMOTE or ADASYN

I am currently working on a binary time-series classification problem using the keras deep learning library. The dataset that I am working with is heavily ...
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23 views

Undersampling for credit card fraud detection before or after Train/Test Split

I have a credit card dataset with 98% transactions are Non-Fraud and 2% are fraud. I have been trying to undersample the majotrity class before train and test split and get very good recall and ...
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Is logrank test and its variations affected by the inequality of classes?

I am currently dealing with an home made exercise which compares the lifetime of patients who made use of a specific drug or not. For simplicity, I decided to remove censored data, but as replacement ...
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How to deal with that accuracy of minority class improves while majority class decreases when using imbalance learning?

I use boosting tree to make prediction for the stock direction, and it is a binary class classification. The majority class is the down direction, and the minority class is the up direction. The ...
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F1 weighted vs. Log loss in SciKit learn RandomSearchCV

I am sorry to ask another question regarding this topic but I am still puzzled about the following: When I use 'F1_weighted' as my scoring argument in a ...
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1answer
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ML test accuracy higher than training? Small unbalanced samples were stratified by class

My background is in ecology, it is common to have smaller sample sizes and class imbalances and ML approaches are still increasingly adopted. My specific dataset: training set is 49 sample, my test ...
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What is Relative Equilibrium Distribution

Some datasets collected are imbalance in nature which violates the assumption of relative equilibrium distribution for most classifier learning algorithms, which will reduce the performance of ...
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Dropping examples in one vs all classification training

I am working on some legacy code where I have K One vs All classifiers for a classification problem having K labels. The dataset is imbalanced(i.e. the ratio of the ...
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Why do accuracy and MacroF1 decreases and AUC increases when I go from transductive to inductive classification?

I have two experiments with the goal of binary classification with multi-layer perceptron. Train, validation, and test set are the same in both. The only difference is that in the 1st experiment, the ...
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1answer
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Handling imbalanced data for regression based tasks

I have an imbalanced google analytics dataset. I'm interested in predicting total.tranactionRevenue but, of the 70,000 data points only 700 have transactions. The value of these transactions varies ...
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27 views

Perfect recall but moderate precision due to imbalance?

I have a patient dataset on which I trained a RF classifier to predict whether a patient ends up in the hospital or not. Nevertheless, this dependent variable is imbalanced (66% of the patients ended ...
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Plot of Decision Boundaries intuition for different SVMs

I have a class imbalanced dataset and I used two different SVMs for binary classification. One plain SVM and one class weighted(i overweighted the positive class). Below are the decision regions I ...
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GAMM input: can it have different lengths?

I am building a GAMM model on the difference between two types of utterances. F1 refers to the first formant of the utterance, which is a continuous variable. Utterance refers to the two different ...
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51 views

Is it ok a threshold of 0?

I am dealing with a classification problem with a dataset containing 60k rows: 69k are negative class, and 1k is positive. I trained my models and I obtained the confusion matrices with a threshold of ...
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correcting for extremely downsampled data: keras class_weight is hurting my model

I have an extremely imbalanced dataset (millions of times more negatives) for a binary classification NN model. I am aggressively downsampling solely for the purpose of making training time manageable,...
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27 views

How can I adjust predicted probabilities after resampling?

I have a real-world problem with severe imbalanced classes. I was able to get a good AUC and balanced accuracy after the implementation of a resampling technique. Now I want to "walk over the ROC&...
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roc auc for small class imbalance

I have a classification problem with class imbalance(1:6). I'd like to know if roc_auc is a valid metric for this level of imbalance. I know it's not good for severe imbalance, but what about a case ...
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Factorial Mixed ANOVA Unequal Gender Ratio [duplicate]

For my thesis I'm doing a factorial Mixed ANOVA in which I look at gender differences. As it's a mixed ANOVA, everyone goes through the same conditions. The problem however is that I have 10 men and ...
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ROSE (Random Over Sampling Examples) in python

I am currently working on imbalanced data topic. And I found a function in R called ROSE (paper). I understand from a high level how the function works, ...
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Dealing with classes in an imbalanced dataset

I have a dataset of continuous features and 4 classes. The classes counts are 1793, 246, 103 and 102. Adding data is quite difficult now. I've done classification with a random forest on the entire ...
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1answer
26 views

Precision and Recall for highly-imbalanced data

I have an imbalanced data with binary label where there are only 4% positive labels among all examples. I want to evaluate my model on the dataset, and I wonder what is the best way (best metric) to ...
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34 views

Low precision after smote

I am working on a classification problem with class imbalance. I implemented every kind of balance technique and I always get high accuracy, recall and roc (0.85) and low precision( around 0.50). Also ...
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Interpretation of confusion matrix after use of classe_weight in Logistic Regression

I am running a test to verify the effect of the unbalanced classes in binary classification. I am using logistic regression with L1 penalty for classifying (I optimised the penalty through CV and ...
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What does it tell me when my ensemble learners (classifier trees) are just as good with only one split?

Basically new to decision trees and ensemble classifiers. Looking for guidance based on what I am seeing. My goal really isn't to use a decision tree. It is to do a simple binary (A/B) ...
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Should I balance my dataset for binary classification?

I am running through a binary classification problem. My target variable is unbalanced the 1s are the 13% of the whole dataset and 0s 87%. Total number of observation 697, number of features 709. ...
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1answer
37 views

Is there an issue using an imbalanced covariate (not dependent) in logistic regression?

I am investigating data from a randomised control trial, where treatment allocation is done on a 2:1 ratio (2 patients on the experimental treatment for every 1 patient on placebo). 400 on ...
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1answer
37 views

A question about a logistic regression classifier performance (with and without resampling)

I am working on a dataset with 20 independent variables and 41188 instances. The task is a binary classification where the target variable has 36548 number of no's and 4640 of yes's. I have used ...
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14 views

How to analyze data from unbalanced fractional factorial design

I ran a 2x2x3 mixed fractional factorial design that collected reaction times for detecting targets across depths. Within-subject IVs: Cue depth (levels: near, mid, far) Depth validity (levels: ...
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58 views

Calibrating probability thresholds based on ROC curve for multiclass classification

I have built a network for the classification of three classes. The network consists of a CNN followed by two fully-connected layers. The CNN consists of convolutional layers, followed by batch ...
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50 views

Resample Unbalanced sequence data in deep learning doesn't have good effect?

I'm working on text classification using a deep learning approach. Because the data I use has unbalanced conditions, I try to implement data balancing techniques using the imblearn library. However, ...
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33 views

Is the PR AUC invariant under label flip?

The ROC-AUC curve is invariant under a flip of the labels. I don't know if it's a famous result, so I will give the proof below. My question is if the PR-AUC curve also has this property. I have not ...
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10 views

if i want well calibrated probabilities but have class imbalance what metric?

i am having some issues on trying to get a correct metric for an imbalanced problem. it is a credit risk problem where i am trying to predict default of a company so i care about probability output. i ...
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1answer
37 views

Dropout in highly unbalanced longitudinal data (WGEE)

I have found a lot of software and examples that uses Weighted Generalized Estimating Equations to deal with missing data in a balanced data set (equal time points). However, I have a very high ...
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66 views

High Validation F1 score but low testing F1 score

I am working on a dataset related to an insurance company and the objective is to predict if the insurance buyer will claim their travel insurance or not. Training data: https://raw.githubusercontent....
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1answer
32 views

Which metric to use to evaluate highly imbalance classification model performance

I have to do classification model to predict the possibilities of person getting cancer based on certain attributes. The data is highly imbalanced. As per client requirement I have to report model ...
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50 views

Binary classification, imbalanced dataset optimization: AUC vs logloss

I'm running optimization on an imbalanced dataset and need to define my optimization metric. I'm working on disease detection so maximizing AUC might not be the best solution, as the certainty of the ...
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23 views

The effect of an imbalanced dataset on multi-class log loss in an imbalanced population

I have sampled data and labeled it as being 1 of 14 classes. This dataset is very imbalanced, e.g. I have a lot of samples for class 1 and not that many for class 14. However, this same imbalance is ...
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52 views

Unbalanced dataset classification problem

I have a binary classification problem and I'm working with an unbalanced dataset. The count for each class in the training set looks like: ...
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25 views

How to tune an weighted voting ensemble method?

I am working on kidney cancer patients' data with 5 unbalanced labels. These codes are contained of Normalization, Oversampling on Feature Engineering part. A list of 9 ordinary Machine Learning ...
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18 views

I wonder why the precision of 1 class is much lower on the test set relative to the training set?

I used the LightGBM algorithm to predict the outcome of tennis matches. Next I made two confusion matrices, one for the training set and one for the test set. I calculated stats as precision, recall, ...
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Will very uneven sample sizes within a factor variable cause problems when running a binomial glm? [duplicate]

I'm running a binomial GLM in R. The data for the model comes from survey responses. The response variable is 'change in wellbeing' and the predictor variables are derived from several other questions ...
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crossvalidation “balancing” for regression problems

Classification problems can exhibit a strong label imbalance in the given dataset. This can be overcome by subsampling certain class weight attributed weights, which allow for balancing the label ...
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39 views

Classification of Imbalanced and Streaming Time Series Data

I have a question about classification of time series. Data has two features and I want to classify it into 5 classes. We have a stream of data and new data is generated every 5 seconds. Moreover in ...
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30 views

What are the measures to detect underrepresented and over represented classes in an imbalanced dataset

In case of imbalanced datasets, we suppose we have the dataset with the corresponding class frequencies ...
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how can i plot a gini curve?

i am using a scoring metric as below: (gini) ...

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