<p>I got the answer for this question:  </p>

<p>Because the dataset becomes imbalanced. That is why the performance of our classifier decreases instead to decrease. Usually you should have a balance between out positive and negative training.</p>

CC BY-SA 3.0
2014-02-26T17:31:42.847Z
2014-02-26T17:37:28.510Z
2014-02-26T17:37:28.510Z
Christina
<p>I am trying to compute the accuracy of the <a href="http://maxlab.neuro.georgetown.edu/hmax/" rel="nofollow">HMAX model</a>.
I am using the <code>Face</code> category (containing 435 images) from the <a href="http://www.vision.caltech.edu/Image_Datasets/Caltech101/" rel="nofollow">Caltech101 database</a>. I split it into $x$ training and $y$ testing. At each time, when $x$ increases, the accuracy also increases. Furthermore, I heard that the number of training should be equal to 80% by comparing it to the tests. So when I split my data into 348 positive training and the rest for positive testing, I got an accuracy that it is smaller than the other smaller splits (when $x&lt;348)$!</p>

<p>By the way, I also used the <code>Background</code> category and I split it into 50 negative training and 50 negative testing.</p>

<p>Why do I get smaller accuracy?</p>

CC BY-SA 3.0
2014-02-15T16:17:39.220Z
how are you defining "accuracy"?
Memming
<computational-modeling><neural-network>
Why do I get smaller accuracy when I use 80% of training sets using HMAX model?
Christina
# Why do I get smaller accuracy when I use 80% of training sets using HMAX model?

(Asked by: username_0 on 2014-02-14)

I am trying to compute the accuracy of the [HMAX model](http://maxlab.neuro.georgetown.edu/hmax/).
I am using the `Face` category (containing 435 images) from the [Caltech101 database](http://www.vision.caltech.edu/Image_Datasets/Caltech101/). I split it into $x$ training and $y$ testing. At each time, when $x$ increases, the accuracy also increases. Furthermore, I heard that the number of training should be equal to 80% by comparing it to the tests. So when I split my data into 348 positive training and the rest for positive testing, I got an accuracy that it is smaller than the other smaller splits (when $x<348)$!

By the way, I also used the `Background` category and I split it into 50 negative training and 50 negative testing.

Why do I get smaller accuracy?

**Tags:** computational-modeling, neural-network

> **Comments:**
> **username_1**: how are you defining "accuracy"?  

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## 1 answers

### Answer by username_0 on 2014-02-26. Score: 1

I got the answer for this question:

Because the dataset becomes imbalanced. That is why the performance of our classifier decreases instead to decrease. Usually you should have a balance between out positive and negative training.


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