<p>I'm experimenting with using CART (or maybe Random Forest) to classify genomic data.
There are essentially two classes, whereof one is the 'normal' state and the other is the 'exceptional' state.</p>

<p>Now, I don't know how to properly set up a training set, because the 'normal' class appears with a much higher frequency (about factor 1000) than the 'exceptional' class.</p>

<p>In order to get the CART to work properly, do I rather use a 1:1 ratio of the samples in my training set or keep the 1000:1 ratio?,</p>

<p>I'm quite a beginner in this field, so any help is appreciated.</p>

CC BY-SA 3.0
2011-09-20T22:08:48.490Z
This might be better suited on stats.stackexchange.com
Suresh Venkat
CC BY-SA 3.0
2011-09-21T00:21:55.247Z
You might want to ask on [Biostar](http://biostar.stackexchange.com/).
Aaron Sterling
CC BY-SA 4.0
2024-01-03T13:57:46.350Z
Related: [(Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?)](https://stats.stackexchange.com/questions/357466/are-unbalanced-datasets-problematic-and-how-does-oversampling-purport-to-he) [(Profusion of threads on imbalanced data - can we merge/deem canonical any?)](https://stats.meta.stackexchange.com/questions/6349/profusion-of-threads-on-imbalanced-data-can-we-merge-deem-canonical-any) [(When is oversampling preferable to undersampling and vice versa?)](https://stats.stackexchange.com/a/611410/247274)
Dave
<machine-learning><decision-trees>
Designing an appropriate training set for CART classification using imbalanced data
Rainer
# Designing an appropriate training set for CART classification using imbalanced data

(Asked by: username_0 on 2011-09-20)

I'm experimenting with using CART (or maybe Random Forest) to classify genomic data.
There are essentially two classes, whereof one is the 'normal' state and the other is the 'exceptional' state.

Now, I don't know how to properly set up a training set, because the 'normal' class appears with a much higher frequency (about factor 1000) than the 'exceptional' class.

In order to get the CART to work properly, do I rather use a 1:1 ratio of the samples in my training set or keep the 1000:1 ratio?,

I'm quite a beginner in this field, so any help is appreciated.

**Tags:** machine-learning, decision-trees

> **Comments:**
> **username_1**: This might be better suited on stats.stackexchange.com  
> **username_2**: You might want to ask on [Biostar](http://biostar.stackexchange.com/).  
> **username_3**: Related: [(Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?)](https://stats.stackexchange.com/questions/357466/are-unbalanced-datasets-problematic-and-how-does-oversampling-purport-to-he) [(Profusion of threads on imbalanced data - can we merge/deem canonical any?)](https://stats.meta.stackexchange.com/questions/6349/profusion-of-threads-on-imbalanced-data-can-we-merge-deem-canonical-any) [(When is oversampling preferable to undersampling and vice versa?)](https://stats.stackexchange.com/a/611410/247274)  

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## 0 answers

