fast_scale {dataPreparation} | R Documentation |

## scale

### Description

Perform efficient scaling on a data set.

### Usage

```
fast_scale(data_set, scales = NULL, way = "scale", verbose = TRUE)
```

### Arguments

`data_set` |
Matrix, data.frame or data.table |

`scales` |
Result of function |

`way` |
should scaling or unscaling be performed? (character either "scale" or "unscale", default to "scale") |

`verbose` |
Should the algorithm talk? (Logical, default to TRUE) |

### Details

Scaling numeric values is useful for some machine learning algorithm such as
logistic regression or neural networks.

Unscaling numeric values can be very useful for most post-model analysis to do so set way to "unscale".

This implementation of scale will be faster that `scale`

for large data sets.

### Value

`data_set`

with columns scaled (or unscaled) by **reference**. Scaled means that each
column mean will be 0 and each column standard deviation will be 1.

### Examples

```
# Load data
data(adult)
# compute scales
scales <- build_scales(adult, cols = "auto", verbose = TRUE)
# Scale data set
adult <- fast_scale(adult, scales = scales, verbose = TRUE)
# Control
print(mean(adult$age)) # Almost 0
print(sd(adult$age)) # 1
# To unscale it:
adult <- fast_scale(adult, scales = scales, way = "unscale", verbose = TRUE)
# Control
print(mean(adult$age)) # About 38.6
print(sd(adult$age)) # About 13.6
```

*dataPreparation*version 1.1.1 Index]