# What is the error histogram in neural network matlab?

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nayoung park 2019년 12월 6일
댓글: Deepthi Deepthi 2022년 3월 9일
I asked yesterday, but I don't understand. because I used neural network matlab tool first time
1. error values don't have to minus value??
2. What is the y axis? is it the number of traning, validation and test data set?
3. why the highest bar exist? is it mean highst bar has 0.001502 error values?
4. what is zero error? I don't understand zero error line means. it means 0? it means distribution which bar close 0?
Is it best performance??? what does mean in this graph??
I'm sorry for many questions.. Thank you in advance.
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Aasma aslam 2021년 4월 3일
how I can get data for this error histogram? my FFNN is creating an error histogram automatically? How i can get the data that is used to plot this histogram?

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### 답변(3개)

Sourav Bairagya 2019년 12월 10일
Error histogram is the histogram of the errors between target values and predicted values after training a feedforward neural network. As these error values indicates how predicted values are differing from the target values, hence these can be negative.
Bins are the number of vertical bars you are observing on the graph. The total error range is divided into 30 smaller bins here.
Y-axis represents the number of samples from your dataset, which lies in a particular bin. For example, at the mid of your plot, you have a bin corresponding to the error of 0.001502 and the height of that bin for training dataset lies below but near to 150 and validation and test dataset lies between 150 and 200. It means that many samples samples from you different datasets have an error lies in that following range.
Zero error line corresponding to the zero error value on the error axis (i.e. X-axis). In this case zero error point falls under the bin with centre 0.001502.
To get more details you can leverage this link:
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usha chintalapati 2020년 5월 15일
Can you please explain this plot?
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Mohit Nair 2021년 5월 13일
Error histogram is the histogram of the errors between target values and predicted values after training a feedforward neural network. As these error values indicates how predicted values are differing from the target values, hence these can be negative.
Bins are the number of vertical bars you are observing on the graph. The total error range is divided into 20 smaller bins here.
Y-axis represents the number of samples from your dataset, which lies in a particular bin. For example, at the mid of your plot, you have a bin corresponding to the error of -0.04225 to 0.04225 and the height of that bin for training dataset lies below but near to 40 and validation and test dataset lies between 50 and 60. It means that many samples samples from you different datasets have an error lies in that following range.
Zero error line corresponding to the zero error value on the error axis (i.e. X-axis). In this case zero error point falls under the bin with centre 0.04225.

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Abhilash Singh 2022년 2월 20일
In addition to the above explanation, the readers may also visit the following article to know how to interpret the error histogram (https://www.mdpi.com/1424-8220/22/3/1070).
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Deepthi Deepthi 2022년 3월 9일
How to plot the error histogram for a classification problem?
In a multiclass classification problem the outputs are labelled as different classes For eg: class1: 1 0 0 0,
class2: 0 1 0 0, class3: 0 0 1 0, class4: 0 0 1 0 etc. (or sometimes it is labelled as 1,2,3,4....for each classes).
From this how to calculate the error function. Is there any solution or is it valid for a classification problem?

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