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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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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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33 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
26 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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0answers
17 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
22 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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0answers
16 views

SMOTE algorithm in R

I have a couple of doubts regarding SMOTE in R using the package DMwR. I am getting new attributes, one without any name and another named as 'X'. These two are completely new after applying SMOTE ...
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1answer
47 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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23 views

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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0answers
20 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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0answers
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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0answers
20 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
11 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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13 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
18 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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0answers
28 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 ...
3
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1answer
54 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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0answers
13 views

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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0answers
8 views

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
25 views

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
26 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
44 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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0answers
25 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
28 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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31 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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15 views

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
18 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
158 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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0answers
14 views

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
41 views

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
43 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-...
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1answer
71 views

Balanced data, but unbalanced result

I'm fairly new to data science. I have a multi-class classification problem with 4 classes, 100K rows. The problem is that the classes are balanced but the prediction results are not. (All 4 classes ...
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24 views

Micro- or macro-averaged AUC for highly imbalanced data?

I have a classification problem with 3 classes. With random forest classifier I'm getting the following confusion matrix: The micro-averaged AUC is 0.76 and the macro-averaged AUC is 0.55. On the ...
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1answer
64 views

re-analysis of someone else's data: 5 treatments x 4 non-exclusive outcomes

I am trying to re-analyze (ETA: used loosely; the original study performed no statistical analysis) some published biological data (below). They used 5 treatments and scored presence/absence of 4 non-...
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27 views

How to create an imbalanced dataset without leading to classifier overfitting

I am working on semi-supervised multi-label classification method that intrinscly deal with the imbalance problem, commonly present in multi-label datasets. That's why, i want to create an imbalanced ...
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1answer
16 views

How to deal with imbalanced data using logith algorithm

I have an imbalanced dataset for predicting bankrutptcy using the logit algorithm. My sample has 2%(200) bankrupt firms. Unfortunately my prediction is worthless with an auroc of 0.52. On top of that ...
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29 views

Why does my precision, recall, f1score and accuracy decreases when I am feeding the model with Upsampled dataset?

I am currently working on a dataset which is imbalanced. It has 2850 negative label points and 483 positive label points and I do upsample on the dataset to balance it. But my model performance ...
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22 views

Binary Class Imbalance - not enough of class A or too much of class B?

Assume 1000 instances of both class A and class B are sufficient to train a decent binary classifier. Since it is easy to get more class B data, we get 99000 additional instances of class B (if we ...
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1answer
25 views

Use Logistic Regression to Predict Click Through Rate

The goal is to predict click through rate of article content. Currently, the linear regression is used and the input data set is at article level. The label is the click rate of the article. The issue ...
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32 views

Oversampling for multi-class neural net

Does this make sense or do I have no idea what I'm doing? I want to train a model that takes a sentence and outputs a binary multi-class vector of size $K$ where each dimension is a question class. ...
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0answers
10 views

Classification of temporal correlated data in out of fold prediction - Surprisingly high Accuracy

At the very moment, I might have awesome results or a problem. I will start with an overview about my problem setting. I have temporal correlated data (~10000 observations, ~200 features) from two ...
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7 views

Motivating the exclusion of certain results based on sample size

A survey of the physical condition of cars on the road is taken every year. A random sample of those cars is selected each year. The number of cars selected each year changes. Most years the sample ...
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2answers
95 views

Data augmentation or weighted loss function for imbalanced classes?

I have a CNN image classification problem with imbalanced classes. I could balance the dataset using data augmentation (Replication, mirror, etc.) on the minority classes. Also, imbalance could be ...
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24 views

Poor P-R curve for binary classifier trained on balanced data, with imbalanced test data

I have a very imbalanced dataset (9:1), for which I have performed under-sampling and achieved a balanced training set (~130k samples total post balancing). I am performing classification using ...
2
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1answer
21 views

How to calculate the probability that a feature falls into a certain class

There are classes A,B,C,D,E. Variable x has different means for each of these classes, but there is overlap in the range of x among classes. Item counts are different between classes, eg. there are ...
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3answers
69 views

Confidence in precision in the presence of few positives

I would like to report precision and recall for an existing binary classifier, which is a black box and which I cannot modify. I have a 1,000 test examples, sampled from a population of 100,000, and ...
2
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1answer
43 views

Get the number of weak learner - ebmc package of R - implementing class imbalance RusBoost on my dataset

I'm new to class imbalance and applying class imbalance technique 'RusBoost' on my dataset. I'm using ebmc package from R. I'm having difficulties to get its arguements values, as per the ...
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1answer
39 views

Interpreting precision and recall graphs

i have a CNN for sentiment analysis whose precision and recall for validation data over 10 training epochs is (average:macro): The dataset contains more positive samples than negatives.I have ...
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39 views

Can unbalanced classes introduce bias in a Random Forest model?

I am working on a classification problem using Random Forest. The training set has 600 instances and 16 attributes. The final class is an Yes/No answer. The ratio of "Yes" to "No" in the training ...
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0answers
77 views

Performance gap between training and validation/test data

I have a heavily imbalanced binary classification task, where the prevalence of the negative event is around 3%. I shuffled then stratified split my data in train, test and validation, ensuring that I ...