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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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Imbalanced multiclass classification with many classes

I am working on a text classification project in which we have hundreds of (imbalanced) classes. Some characteristics of the data: We have examples of "bad" documents. Basically documents that don't ...
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Imbalanced class SVM prediction results using different validation data

I am trying to fit my data to a classifier using SVM. My data has 2 classes, the positive class which occurs with a probability of 0.002 and the negative class which is the dominant one. Suppose that ...
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Question about sample size for each class for machine learning classifiers

I'm trying to use a machine learning classifier (SVM in particular) on data that I generate. Unlike other applications, the data is not given to me but rather I have the flexibility to generate how ...
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23 views

Dealing with dataset imbalance: test if adjusting is necessary

I'm currently working on a project which uses a imbalanced dataset (two classes) for training, and I'm not sure if I should do a resampling procedure or not. Is there a way to actually test if it's ...
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4 views

State of the art in feature extraction from review text

I am working on a sentiment review classification problem and so far i have explored POS tags, synsets, N-grams, word2vec, tf-idf, doc2vec, glove and fastext vectors as features. I am wondering what ...
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28 views

Imputing and handling class imbalance

I have data with missing values. My $y$ is imbalanced (20% to 80%). a) is it at all possible to balance (e. via Smote) and Impute (e. via Mice) or will the results become too unreliable? b) if a) ...
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30 views

Imbalanced Test Data

I have an imbalanced (1:5) training and test set with only two classes and have oversampled the training set with SMOTE so that the class ratio is 1:1. The ML model gives values over 0.7 for accuracy, ...
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13 views

Encoding variable number of categorical features

I have a dataset listing the software installed for each user. This dataset shall be used (in conjuction with other user datasets) to classify the user into 4 (imbalanced) categories. There are over ...
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12 views

Power rank/Composite score help needed

I've been assigned to create a power rank for 160 call centre market research employees (called interviewers). Getting more into it, I realise the necessity of statistics. Now, for some reason, when I ...
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8 views

Different outcomes of the classification algorithms with similar AUC score

I am new to applied statistics and have a question to some more experience folks regarding the outcome of the models I've built. My data is highly imbalanced and I use oversampling to even out the ...
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1answer
22 views

Bernoulli Naive Bayes with Unbalanced Classes of Binary Features

I'm using a multivariate Bernoulli model with a Naive Bayes classifier on binary features. It's giving a lot of very low estimates of the probability that each instance is correctly classified. I ...
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23 views

Alternatives to logistic regression when dataset can't meet 10 events per predictor variable

I have a dataset with a binary outcome I'd like to model, but have many more parameters than events, probably ~100 categorical parameters to test, in a dataset with 20 events in ~600 observations. As ...
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19 views

skewed data Distribution

I have a dataset with 97% of dependent variable of 0's, and 3 % of 1's. Want to apply machine learning models to predict output. What are the techniques of balancing or combating tactics before ML ...
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1answer
33 views

using (deep) neural networks for a severely imbalanced image dataset when some classes have <10 images

Taking a long shot here. So I have a a small dataset of ~500 images with discrete labels from 1 to 9. My task is to detect the per-class and overall accuracy of this classification method using a (...
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6 views

Binary Classification - Why is the Ratio of FP to TP the same (and can i leverage this fact?)

I am training a highly imbalanced dataset on a binary classification problem. (about 1.2% positive class). I have tried neural nets, random forest classifier, and gradient boosting classifier from ...
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20 views

Difference between AUPRC in caret and PRROC

I'm working in a very unbalanced classification problem, and I'm using AUPRC as metric in caret. I'm getting very differents results for the test set in AUPRC from caret and in AUPRC from package ...
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4 views

Training Heterogeneous ensemble with under sampled and actual data

I have an ensemble created by using 5 different models, I have an imbalanced dataset of 1:4 .I have trained the 3 models with entire dataset and 2 models with random under sampled dataset which yields ...
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28 views

How to handle duplicate samples & unbalanced sample values?

Assume I have the below simplified training data (5 male and 1 female visits), and I want to predict, for a subsequent sample given a set of feature values, what’s the likelihood that “converted” == 1 ...
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Compare prevalence of categorical feature among different size pop

Problem description I have 2 populations, let's say men and women, of very different size. men population size : 100 000 women population size : 130 Each individual in the populations can combine ...
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1answer
29 views

Unbalanced dataset accuracy [duplicate]

I’m currently encountering some problems analyzing a dataset with neural network. The problem is that I have an unbalanced binary class training set (10:1). Training accuracy for both classes are 100%....
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How can stratified kfolds perform worse than regular kfolds?

I am working with unbalanced classes to solve a classification problem (whether individuals pay their fees or not). My class imbalance is 75% positive (paid) and 25% negative (unpaid). I have read ...
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38 views

Handling imbalanced data for classification [duplicate]

What are the best ways to deal with imbalanced datasets for classifying whether or not individuals pay their tuition? The data is 75% positive class (paid) and 25% negative (unpaid). Some approaches I ...
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1answer
33 views

How to deal with missingness of dependent variable in unbalanced probit model

I am trying to estimate a probit model on the probability of suicide over the next year in a population. Unfortunately for this research, suicide rates are very very low so the probability of suicide ...
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31 views

Unbalanced data on fire for a binary classifier

I have a lot of training data from which I want to build a binary classifier, but the classes are highly unbalanced, 97% in one class, 3% in the other (even though, in absolute terms, I still have a ...
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1answer
55 views

How to Interpret AUROC score?

My model has an AUROC value of 0.7, and I have a 75:25 class (75% negative, 25% positive) imbalance. From my understanding, AUROC is calculated by using different thresholds for considering the ...
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1answer
57 views

t-test or paired t-test to detect drift for suicide prediction

Context and data I am studying suicides among the military. I created a table that aggregates certain metrics (number of holidays, number of hours worked, etc...) for each officer, for each month ...
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How to deal with balanced training data and severely imbalanced testing data

My dataset is severely imbalanced, negative class is the majority and its records is almost 10000 larger than the positive class records. I'm using Python. Here's what I have already done: I used ...
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25 views

How to classify similar looking dataset but belonging to different class

I've got a user dataset in which there are two classes. The size of dataset if 50,000. Class_A=5000 , class_B= 45000. Now the problem is that there are some instances(500) which though belong to ...
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9 views

Learning similarity metric from data

I want to measure the similarity of fastText vectors. Typically, for vector similarity, the cosine similarity is used. I would like to learn a notion of similarity based on the labels of the tokens ...
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65 views

How to manually balance unbalanced multi-class/multi-label data?

I have a multi-class and multi-label classification problem, i.e.: each sample can have more than one label associated to it and there is a total number of M ...
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0answers
15 views

multivariate welch test [closed]

Has anyone implemented the use of the multivariate Welch test (https://github.com/alekseyenko/Tw2), implemented in R? I am trying to figure out how to format the inputs and I am not having a lot of ...
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17 views

Python Imblearn - How to track original data records

My data is severely unbalanced, so I'm using Python imbalanced-learn here to make data balanced: https://imbalanced-learn.readthedocs.io/en/stable/generated/imblearn.combine.SMOTEENN.html#imblearn....
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1answer
24 views

Merge one label with one information for classification problem or multi-label classification

I want to build a model to support decision making in order to propose or not loan insurance to clients. Because sometimes clients asking loan and loan insurance have less chance to have their loan ...
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36 views

Classification model on a highly unbalanced dataset [duplicate]

I’m dealing with a highly unbalanced dataset where 20% of data belongs to class A and 80% belongs to class B. It’s very hard for us to produce synthetic class A data. Just wondering if the below ...
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1answer
103 views

Does class balancing introduce bias?

I have a data set that is imbalanced, the prediction rate is not much better than the base line without doing any class balance. I have two classes and I can't collect more data. What I have done: ...
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While dealing with imbalanced classes, to what extent can we upsample a minority class? [duplicate]

I have my training data with the following approximate distribution: Negative events : 90,000 positive events : 5,000 Training a model would require to oversample the minority class (and might also ...
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I am building model for classification and in practice my data could be imbalance to any extend. How could I build single ml model to perform task? [duplicate]

for example: Case 1: Class A:10 Observations Class B: 100 Observation Case2: Class A: 100 Observations Class B: 10 Observations How could I build a single model (Random Forest) to perform this task?
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1answer
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Train on balanced datasets, used for imbalanced datasets?

We usually trained a model using balanced datasets. Even when we do not have a balanced datasets, we will use methods such as SMOTE to create a balanced dataset for training. The question is - how ...
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1answer
32 views

Creating an Imbalanced Dataset

I would like to have my trained model tested on an imbalanced dataset. Is there any algorithms available to generate synthetic data from a balanced labelled dataset (spam/non-spam)?
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1answer
103 views

classification on imbalanced dataset via random forest: results vary with random seed

I have a highly imbalanced dataset of about 8000 observations, with 11 features and one binary target variable. I want to predict the target labels, considering that the "1" target label occurs for 1....
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45 views

Oversampling using SMOTE leading to bad predictions on test set

I have a dataset with an imbalanced binary target. One class accounts for about 94 % of the target variable. I used SMOTE to oversample the minority class but after the oversampling step when I train ...
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1answer
31 views

Is it class imbalance? Test set gives very high proportion of a class which was in minority in train set [closed]

I want to investigate why am I seeing the below described phenomena. I welcome all the logical explanation which might hint towards what is happening. So I have a dataset which contains two classes: ...
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41 views

Dealing with unbalanced data in an explanatory model with decision tree

I am doing binary classification with decision tree, and it aims to find out what features matter the most with the data we have, so I need interpretability more than predictability. It is like ...
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0answers
12 views

Multiple class prediction with unbalanced data [duplicate]

I have 4 different web sites (A/B/C/D) and need to predict whether a user will rather convert on A/B/C or D or not convert at all. My data set has 100000 rows and only 100 of those are converted user (...
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Resampling imbalanced data in a multi-view scenario

Assume a multi-view scenario, where multiple views of the same entity are available. If each data pair is assigned a label and the resulting scenario is highly imbalanced, what are proper ways of ...
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0answers
23 views

Which statistical test to use in R? Unbalanced design with one dependent variable and multiple independent variables

it's been a while since I've had to do any statistics and I need a little help determining which statistical model/test to use in R. My background is more with multivariate stats, so it's been a while ...
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1answer
484 views

XGBoost implementation for unbalanced data using scale_pos_weight parameter

I have a confusion regarding how cost sensitive custom metric can be used for training of unbalanced dataset (two class 0 and 1) in XGBoost. Metric: Cost = 10*#of false positives + 500*# of false ...
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Will the small proportion ruin the ability of chi-square test? [duplicate]

Let me raise an example to be clear: say I have a 2*2 contingency table as follows: 5 10 10000 10000 I assume it satisfies the assumptions of chi-square test. But the proportion ~ 0.05% is very ...
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1answer
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Should we balance the data set if the data is intrinsically unbalanced?

Say I want to predict the cancer rate(regression)/predict the whether a person has cancer or not(classification). The data intrinsically has few cancer patients/low cancer rate, say 1/200. And the ...
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1answer
53 views

Appropriate strength test of the chi-square test for large and unbalanced data

I understand different statistical tools have their own pros and cons. I'm trying to find the most appropriate one for my situation. I have a large, unbalanced data set and want to implement the chi-...