# Data calibration in ANN

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NN il 12 Ott 2023
Risposto: Neha il 16 Ott 2023
Which are the different calibration techniques in ANN that is used in basic ann forecasting model?
Any example files is there to refer?
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NN il 12 Ott 2023
How can i calibrate the data in nntraintool?
Below is the code that i am using.
% Setup Division of Data for Training, Validation, Testing
net.divideParam.trainRatio = 65/100;
net.divideParam.valRatio = 5/100;
net.divideParam.testRatio = 30/100;

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### Risposte (1)

Neha il 16 Ott 2023
Hi,
I understand that you want to know about the data calibration techniques used in ANN. You can refer to the following common calibration (or data preprocessing) techniques used in ANN:
Normalization: This technique scales the data to fit within a certain range, usually 0 to 1 or -1 to 1. You can use "mapminmax" function to normalize your data. You can refer to the following documentation link for more information on "mapminmax":
However if you are using deep learning workflows, you can normalize the data in the input layer itself (Eg: "sequenceInputLayer", "imageInputLayer", "featureInputLayer", etc) using normalization name-value pair instead of using "mapminmax".
Standardization: This technique transforms the data to have zero mean and unit variance. This is useful when the data follows a Gaussian distribution. You can use "mapstd" function to standardize the data. You can refer to the following documentation link for more information on "mapstd":
Apart from the above two techniques, if your data is categorical, you can use the "onehotencode" function to create a binary column for each category and mark it with a 1 for the corresponding category. You can refer to the following documentation link for more information on "onehotencode":
You have also mentioned that you have used "nntraintool" for neural network training, since this function is deprecated, you can use the "train" function instead to train shallow neural networks:
Hope this helps!
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