Linked Questions

4
votes
4answers
2k views

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 ...
1
vote
0answers
2k views

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 ...
22
votes
0answers
431 views

Are unbalanced datasets problematic, and (how) does oversampling (purport to) help? [duplicate]

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 datasets ...
0
votes
2answers
571 views

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 ...
0
votes
1answer
591 views

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 ...
6
votes
2answers
246 views

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 ...
2
votes
1answer
226 views

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 ...
1
vote
0answers
256 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 ...
2
votes
2answers
88 views

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:...
2
votes
0answers
233 views

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 ...
1
vote
3answers
176 views

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 ...
1
vote
0answers
169 views

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 ...
0
votes
0answers
63 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 ...
1
vote
0answers
62 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 ...
0
votes
0answers
45 views

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 ...
1
vote
1answer
36 views

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 ...
0
votes
0answers
34 views

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?
0
votes
1answer
20 views

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 ...
0
votes
0answers
23 views

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(...
0
votes
0answers
23 views

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 ...
1
vote
0answers
23 views

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 ...
0
votes
0answers
18 views

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 ...
0
votes
0answers
16 views

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 ...
1
vote
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 (...
0
votes
0answers
9 views

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....
0
votes
0answers
8 views

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, ...
119
votes
7answers
42k views

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. ...
85
votes
3answers
76k views

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: ...
43
votes
4answers
38k views

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 ...
47
votes
4answers
23k views

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 ...
15
votes
3answers
19k views

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 ...
34
votes
3answers
2k views

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. First let ...
25
votes
4answers
3k views

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 ...
13
votes
2answers
6k views

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 ...
2
votes
2answers
3k views

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 ...
4
votes
1answer
1k 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: ...
1
vote
1answer
2k views

Regression on imbalanced data

I want to predict a continuous value between 0 and 1 and the true labels are 99% (out of 100000 samples) zero and rest of them are between 0 and 1. What are the approaches that I can take so that I ...
1
vote
0answers
971 views

Why would random forest perform bad on unbalanced class

There is a huge number of posts saying that an imbalanced classes are bad. And only half explains it in terms of recall-presicion scores, meaning that accuracy can be high but F1 score low. What I ...
1
vote
1answer
584 views

When to use stratified k-fold

According to a post on Analytics Vidhya: Having said that, if the train set does not adequately represent the entire population, then using a stratified k-fold might not be the best idea. In such ...
1
vote
1answer
599 views

Calibration after up and downsampling

I am experimenting with different techniques to deal with imbalanced classes in a classification problem. I am comparing upsampling the minority class with downsampling the majority class. Furthermore ...
1
vote
3answers
256 views

Machine Learning - How to Sample Test and Training Data for Rare Events

Suppose I have a data set with 1000 observations. I want to train and test a Classification Model to predict a target variable as true or false. However, in my observation set, true occurs only say 10%...
1
vote
0answers
667 views

Effects of class imbalance on nn batch training

Say I have a binary classification task, where the positive class (1) is only 1% of the whole data set. Intuitively I can understand why this could be bad for the classifier as the model may learn ...
1
vote
1answer
435 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, ...
1
vote
3answers
290 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 ...
2
votes
0answers
277 views

How to work with unbalanced datasets in generalized Boosted Regression Models using gbm in r?

I'm using generalized boosted regression models to explore what is the contribution of 20 independent environmental variables (x1, x2, ...., x20) to the explanation of the variability of the dependent ...
1
vote
1answer
166 views

How to deal with a highly unbalanced classification problem?

I currently have data where there are 5 labels, 1,2,3,4,5 for my $Y$ variable and a set of associated predictors $X$. The problem is, I have around $10000$ observations with label $1$ and around ...
0
votes
1answer
122 views

oversampling in nested cross validation

Introduction I have a small mixed dataset consisting of continuous and categorical independent variables with a dichotomous dependant variable. I'm running various algorithms (neural networks, random ...
0
votes
0answers
192 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 ...
0
votes
0answers
189 views

Balanced LogLoss with XGBoost

Following the discussion on here I started worrying less about class imbalance. However, I recently started building a predictor, using XGBoost, and I wanted to used LogLoss as my target metric. I ...
1
vote
0answers
65 views

How do you deal with imbalanced data when you're doing regression?

To describe my problem. I'm predicting the price of an item depending on some text and other features in an ad. The training data contains a bunch of cheap items, some medium price items and few ...

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