This feature preprocessor object finds the mean and standard deviation using the training data. The preprocessor then z-score transforms the training and test data using this mean and standard deviation by subtracting the mean and dividing by the standard deviation.
fp_zscore(ndr_container_or_object = NULL)The purpose of this argument is to make the constructor of the fp_zscore feature preprocessor work with the pipe (|>) operator. This argument should almost never be directly set by the user to anything other than NULL. If this is set to the default value of NULL, then the constructor will return a fp_zscore object. If this is set to an ndr container, then a fp_zscore object will be added to the container and the container will be returned. If this argument is set to another ndr object, then both that ndr object as well as a new fp_zscore object will be added to a new container and the container will be returned.
This constructor creates an NDR feature preprocessor object with the
class fp_zscore. Like all NDR feature preprocessor objects, this feature
preprocessor will be used by the cross-validator to pre-process the
training and test data sets.
This feature preprocessor object applies z-score normalization to each feature by calculating the mean and the standard deviation for each feature using the training data, and then subtracting the mean and dividing by the standard deviation for each feature in the training and test sets. This function is useful for preventing some classifiers from relying too heavily on particular features when different features can have very different ranges of values (for example, it is useful when decoding neural data because different neurons can have different ranges of firing rates).
Other feature_preprocessor:
fp_select_k_features()
# The fp_zscore() constructor does not take any parameters. This object
# just needs to added to a list and passed to the cross-validator applied
fp <- fp_zscore()