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15.10. Data Transformation before Training?

Applies to: NeuralTools 5.x–7.x

Is it advisable to transform some or all variables mathematically before inputting the dataset, for instance by a log transformation?

NeuralTools automatically scales numeric input variables linearly, to reduce differences in the order of magnitude of the variables; this is described in the manual.

Depending on your data, you may improve your results by performing additional non-linear transformations of data before training and prediction. For example, if the distribution of a variable has a long tail with most of the data points clustered together, the log transformation may be useful. The objective is for the neural net to "learn" how different patterns in inputs relate to the outputs, and that process is less likely to succeed if differences in patterns are obscured by data points being clustered together.

NeuralTools itself is not set up to do any user-selected data transformations. However, if you also have StatTools you can use it to perform several common types of non-linear transformations and then paste the results into your NeuralTools input data.

Last edited: 2015-09-03

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