Data preparation for NeurEco Classification with python API

The python API expects the data for model construction or evaluation in form of NumPy arrays containing the data.

• allowed types of arrays: int, float, double

• input array contains a table with:

  • number of lines equal to a number of samples

  • number of columns equal to a number of input features

• output array contains a table with:

  • number of lines equal to a number of samples

  • number of columns equal to a number of output features, for Classification these features are the classes

  • the output is one-hot encoded: each line contains ‘0’ on all positions, except for one containing ‘1’. This position corresponds to a class to which belong the sample on the line.

• input array and the corresponding output array have the same number of samples

There is no need to normalize the data, as the normalization is handled by NeurEco, Data normalization for Tabular Regression.