Linked Questions

135
votes
9answers
54k 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. ...
91
votes
3answers
85k 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: ...
56
votes
7answers
30k views

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 ...
48
votes
4answers
24k 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 ...
47
votes
5answers
44k 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 ...
39
votes
3answers
3k 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. ...
28
votes
4answers
5k 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 ...
27
votes
0answers
649 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 ...
16
votes
3answers
21k 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 ...
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 ...
7
votes
2answers
340 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 ...
4
votes
1answer
2k 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: ...
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 ...
4
votes
2answers
67 views

Should we really do Re-Sampling in Class Imbalance data?

I have been doing ML for quite some time and I have a thought in class imbalance problems that has bothered me quite a lot. In problems where we have Imbalanced Dataset (one class is far more frequent ...
3
votes
1answer
138 views

Running SMOTE on very large class imbalanced datasets - batched or subsampled implementations

There is a theoretical and computational aspect to this question. I was trying to use SMOTE to reduce class imbalance in a rather large dataset--about 8 million rows. The data has a binary outcome ...

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