Linked Questions
101 questions linked to/from When is unbalanced data really a problem in Machine Learning?
4
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How to handle data imbalance in classification? [duplicate]
I am working on a text classification problem. My data is highly imbalanced. For example, one category has 700 documents while the other has 30. I have around 30 categories. I tried different ...
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8
votes
2
answers
1k
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Should we balance the data set if the data is intrinsically unbalanced? [duplicate]
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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vote
0
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2k
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Repeating rare examples in unbalanced data classification [duplicate]
So I'm trying to train a neural network for a rare event detection. Based on that, I have like 1000 times more examples for non-target (everything else) examples that I have for target examples. So I ...
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0
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2
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627
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Machine Learning with Skewed Classes in R [duplicate]
I am looking for some suggestions on what methods are appropriate for training a dataset with a high skew in the outcome classes. The ratio of Class 0: Class 1 is about 20:1 and I am looking to ...
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0
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1
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Does it make sense to up-sample imbalanced data if I care about predicting the right probabilities? [duplicate]
I'm developing a predictive model on a dataset of about 25K observations with the response variable having ~60 classes. There are ~130 predictor variables, all of them binary. In this problem I care ...
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2
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1
answer
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When do we speak of imbalanced data? [duplicate]
I was wondering, what the threshold is for a dataset to be called 'imbalanced'?
Technically every dataset where the target-classes aren't evenly distributed is not balanced. Yet small imbalance ...
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2
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2
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251
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Does the ratio of training data (significantly) affects the result? [duplicate]
Say I have two training data of email titles with 10000 entries each. One of them have 2000 ham and 8000 spam, while the other have 5000 ham and 5000 spam. Will predicting any random email with the 2:...
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0
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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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2
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Problems with classification in imbalanced datasets [duplicate]
I often read about the problematic of doing classification in imbalanced datasets and methods to address it. Namely, off-the-shelf classifiers learn to minimize some form of total miss-clasffication ...
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1
vote
3
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Binary classification with imbalanced classes [duplicate]
I have a manufacture dataset of 65 million rows corresponding to 65 millions distinct items.
Out of those 65 millions, I have 60,000 of them that failed a certain test, thus I have very imbalanced ...
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2
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0
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Why imbalance in the data set is an issue in data mining and machine learning? [duplicate]
I've analyzed a data set from a credit card company before and it has the famous "unbalanced classes" problem like all the other credit card companies, i.e., in the data set (the information of users ...
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1
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0
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positive and negative sample count for ConvNets [duplicate]
I have been trying to set up a ConvNet to classify some data. This data should be classified to either 1 (being what I need to get from the image) and 0 for everything that is irrelevant. I have ...
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0
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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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0
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0
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143
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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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Unbalanced groups and classification errors [duplicate]
I would like to adopt a general strategy for dealing with an very unbalanced dataset, where my "positive" group corresponds to 1/40 of all the observations. The reason why I ask it is because all the ...
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3
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0
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How to make use of less data of a particular class for better modeling? [duplicate]
I have a dataset, say 9000 rows, with some features. Around 8000 belong to class 1 and 1000 to class 0. So, if I am creating a model with any method say SVM, LR, Random forest the model has a tendency ...
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1
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logic behind balancing? [duplicate]
I am a newbie in stats, and while reading: https://towardsdatascience.com/having-an-imbalanced-dataset-here-is-how-you-can-solve-it-1640568947eb
I don't seem to understand why is an imbalanced ...
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1
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Problem of unbalanced data [duplicate]
unbalanced data is an issue that can effect the performnce of classification model ,several remides can be done to balance the data two of them are upsampling and downsampling , my questions is :
how ...
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1
vote
1
answer
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Cross Validation in an Imbalanced data set [duplicate]
What is the point of oversampling an imbalanced data set if the ratio of the classes needs to be preserved in Cross validation ? If I have 1000 rows in a data set where 800 rows belong to one class ...
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0
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Choosing better samples for downsampling [duplicate]
I'm training a classification model for highly unbalanced data where hits are pairs that have similarity of almost 1 on independent metric. Everything else are non hits. Does it make sense to pick ...
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After applying SMOTE, the class distribution doesn't match the real world. Is this a problem? [duplicate]
I have an extremely unbalanced dataset with two classes:
1: 1,800 # class 1
0: 40,000 # class 0
This is real world customer data of churned/not churned
If I ...
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0
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0
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unbalaced data set in classification tasks [duplicate]
How do I judge whether the dataset is unbalanced (is it when the minority class inferior of 15%) could us use the test CR after balancing the data?
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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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Deal with imbalanced data [duplicate]
Building machine learning models to do forecast, sometimes the dataset was used is imbalanced, and there are some methods to deal with this issue such as the resample method and choose other metrics(...
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1
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In a logistic regression model focused on prediction, what are the problems associated with a big difference in the sample sizes of a binary variable? [duplicate]
In short, I'm curious about the problems associated with a difference in the sample size of respondents by a binary variable when fitting a logit model focused on prediction rather than causality.
By ...
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Risks of disproportionate numbers of training examples for different classes? [duplicate]
fairly new to machine learning so please be gentle!
I was wondering what the risks might be if I have significantly more examples for one class than the other when training a 2-D perceptron; for ...
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0
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At which threshold is unbalanced data a problem for a binary classification tree? [duplicate]
I want to build a binary classification tree to clasfiy wether a person is working or not and use the model for prediction. I read that unbalanced data could be a problem. Now i ask myself at which ...
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Sampling in case of imbalanced dataset [duplicate]
From a course of AI for Medical Diagnosis, it is explained that validation and test sets should be balanced, 50-50 cases of both cases 0 and 1, so that the performance of the model can be assessed.
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Learning Distribution of Data [duplicate]
Sometimes it's important to generate data due to data imbalance issues. I heard that data augmentation by leaning distribution of data is a hot topic now. Could you please give me some resources and ...
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0
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Will more training data of one class destroy the good model? [duplicate]
I am facing a binary classification problem that I don't know I should use more data or not.
I have one label 'A' with 10 training examples. And another label 'B' with also 10 training examples. By ...
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0
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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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When should one do class rebalancing? [duplicate]
Does anybody know a source when class rebalancing should be considered?
Say one has a very small dataset. About 70 observations.
When would class rebalancing make sense?
When the 0/1 ratio is 70/30, ...
247
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Why is accuracy not the best measure for assessing classification models?
This is a general question that was asked indirectly multiple times in here, but it lacks a single authoritative answer. It would be great to have a detailed answer to this for the reference.
...
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126
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Does an unbalanced sample matter when doing logistic regression?
Okay, so I think I have a decent enough sample, taking into account the 20:1 rule of thumb: a fairly large sample (N=374) for a total of 7 candidate predictor variables.
My problem is the following: ...
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69
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7
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Binary classification with strongly unbalanced classes
I have a data set in the form of (features, binary output 0 or 1), but 1 happens pretty rarely, so just by always predicting 0, I get accuracy between 70% and 90% (depending on the particular data I ...
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Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?
TL;DR
See title.
Motivation
I am hoping for a canonical answer along the lines of "(1) No, (2) Not applicable, because (1)", which we can use to close many wrong questions about unbalanced ...
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5
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Training a decision tree against unbalanced data
I'm new to data mining and I'm trying to train a decision tree against a data set which is highly unbalanced. However, I'm having problems with poor predictive accuracy.
The data consists of students ...
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54
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Class imbalance in Supervised Machine Learning
This is a question in general, not specific to any method or data set. How do we deal with a class imbalance problem in Supervised Machine learning where the number of 0 is around 90% and number of 1 ...
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What is the root cause of the class imbalance problem?
I've been thinking a lot about the "class imbalance problem" in machine/statistical learning lately, and am drawing ever deeper into a feeling that I just don't understand what is going on.
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1
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Does down-sampling change logistic regression coefficients?
If I have a dataset with a very rare positive class, and I down-sample the negative class, then perform a logistic regression, do I need to adjust the regression coefficients to reflect the fact that ...
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3
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SVM for unbalanced data
I want to attempt to use Support Vector Machines (SVMs) on my dataset. Before I attempt the problem though, I was warned that SVMs dont perform well on extremely unbalanced data. In my case, I can ...
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What problem does oversampling, undersampling, and SMOTE solve?
In a recent, well recieved, question, Tim asks when is unbalanced data really a problem in Machine Learning? The premise of the question is that there is a lot of machine learning literature ...
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Could we explain the disadvantage of imbalanced data mathematically?
Simple setup:
observed response is binary (yes/no, 0/1, positive/negative).
use logistic regression to model the probability of the response being, say, 1: $P(Y=1|X)$.
the MLE of the model ...
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2
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Reference for log-loss (cross-entropy)?
I'm trying to track down the original reference for the logarithmic loss (logarithmic scoring rule, cross-entropy), usually defined as:
$$L_{log}=y_{true} \log(p) + (1-y_{true}) \log(1-p)$$
For the ...
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4
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Data Imbalance: what would be an ideal number(ratio) of newly added class's data?
Assume that I have 10 classes with 100 samples for each class—same # of samples, perfect balanced dataset.
I want to add 3 new classes, and which of the following is the best option for the number of ...
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2
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Bagging with oversampling for rare event predictive models
Does anyone know if the following has been described and (either way) if it sounds like a plausible method for learning a predictive model with a very unbalanced target variable?
Often in CRM ...
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3
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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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Classification algorithms for handling Imbalanced data sets
I’m working on a classification problem where dataset is extremely imbalanced ( roughly 13000 "zero" and 100 "one" responses).
As the first step, I trained a Logistic Regression and changing the ...
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1
answer
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How to balance classification?
I have a binary classification problem, where my training data is 70% positive labeled and 30% negative labelled. I use a logistic loss and it always classifies examples positive on the test data.
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1
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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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