Linked Questions

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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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0answers
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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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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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0answers
11 views

Do I need to treat class imbalance before preforming RFE (Recursive Feature Elimination)? [duplicate]

I am trying to perform RFE on an imbalanced data set that will later be used for binary classification. I have chosen to use the caret::rfe package for feature selection. I am using random forests ...
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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....
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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, ...
112
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7answers
39k 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. ...
81
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3answers
73k 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
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4answers
36k 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 ...
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4answers
22k 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
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3answers
18k 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 ...
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2answers
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 ...
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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 ...
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2answers
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 ...
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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 ...

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