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 ...
25
votes
0answers
563 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
572 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 ...
7
votes
2answers
313 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 ...
0
votes
1answer
663 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 ...
1
vote
0answers
342 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
1answer
257 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 ...
2
votes
2answers
96 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
184 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
177 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 ...
1
vote
0answers
98 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
79 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
1answer
48 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
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 ...
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
21 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
25 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
24 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
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
1answer
21 views

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 ...
0
votes
0answers
18 views

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 ...
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
9 views

Imbalanced data in multi-label classification [duplicate]

I am trying to do MLC using the CnicalBERT pre-trained weights. The data is little biased i.e., some classes more frequently than others. After applying ML-ROS oversampling technique, Mean IRBl ...
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, ...
125
votes
9answers
50k 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. ...

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