preprocess_scale: Scale Data

In mlpack: 'Rcpp' Integration for the 'mlpack' Library

Scale Data

Description

A utility to perform feature scaling on datasets using one of six techniques. Both scaling and inverse scaling are supported, and scalers can be saved and then applied to other datasets.

Usage

preprocess_scale(
  input,
  epsilon = NA,
  input_model = NA,
  inverse_scaling = FALSE,
  max_value = NA,
  min_value = NA,
  scaler_method = NA,
  seed = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

input	Matrix containing data (numeric matrix).
epsilon	regularization Parameter for pcawhitening, or zcawhitening, should be between -1 to 1. Default value "1e-06" (numeric).
input_model	Input Scaling model (ScalingModel).
inverse_scaling	Inverse Scaling to get original datase. Default value "FALSE" (logical).
max_value	Ending value of range for min_max_scaler. Default value "1" (integer).
min_value	Starting value of range for min_max_scaler. Default value "0" (integer).
scaler_method	method to use for scaling, the default is standard_scaler. Default value "standard_scaler" (character).
seed	Random seed (0 for std::time(NULL)). Default value "0" (integer).
verbose	Display informational messages and the full list of parameters and timers at the end of execution. Default value "getOption("mlpack.verbose", FALSE)" (logical).

Details

This utility takes a dataset and performs feature scaling using one of the six scaler methods namely: 'max_abs_scaler', 'mean_normalization', 'min_max_scaler' ,'standard_scaler', 'pca_whitening' and 'zca_whitening'. The function takes a matrix as "input" and a scaling method type which you can specify using "scaler_method" parameter; the default is standard scaler, and outputs a matrix with scaled feature.

The output scaled feature matrix may be saved with the "output" output parameters.

The model to scale features can be saved using "output_model" and later can be loaded back using"input_model".

Value

A list with several components defining the class attributes:

output	Matrix to save scaled data to (numeric matrix).
output_model	Output scaling model (ScalingModel).

Author(s)

mlpack developers

Examples

# So, a simple example where we want to scale the dataset "X" into "X_scaled"
# with  standard_scaler as scaler_method, we could run 

## Not run: 
output <- preprocess_scale(input=X, scaler_method="standard_scaler")
X_scaled <- output$output

## End(Not run)

# A simple example where we want to whiten the dataset "X" into "X_whitened"
# with  PCA as whitening_method and use 0.01 as regularization parameter, we
# could run 

## Not run: 
output <- preprocess_scale(input=X, scaler_method="pca_whitening",
  epsilon=0.01)
X_scaled <- output$output

## End(Not run)

# You can also retransform the scaled dataset back using"inverse_scaling". An
# example to rescale : "X_scaled" into "X"using the saved model "input_model"
# is:

## Not run: 
output <- preprocess_scale(input=X_scaled, inverse_scaling=TRUE,
  input_model=saved)
X <- output$output

## End(Not run)

# Another simple example where we want to scale the dataset "X" into
# "X_scaled" with  min_max_scaler as scaler method, where scaling range is 1
# to 3 instead of default 0 to 1. We could run 

## Not run: 
output <- preprocess_scale(input=X, scaler_method="min_max_scaler",
  min_value=1, max_value=3)
X_scaled <- output$output

## End(Not run)

mlpack documentation built on Feb. 7, 2026, 9:06 a.m.