R: Scale Data

preprocess_scale {mlpack} | R Documentation

## Scale Data

### Description

A utility to perform feature scaling on datasets using one of sixtechniques. Both scaling and inverse scaling are supported, andscalers 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 = 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 "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:

`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)

[Package mlpack version 3.4.2.1 Index]