Standard Scaler
Description
The standard scaler scales the given data set, so that all features will have a user specified mean and variance. In case the user does not provide a specific mean and standard deviation, the standard scaler transforms the features of the input data set to have mean equal to 0 and standard deviation equal to 1. Given a set of input data \(x_1, x_2,… x_n\), with mean:
and standard deviation:
The scaled data set \(z_1, z_2,…,z_n\) will be:
where \(\textit{std}\) and \(\textit{mean}\) are the user specified values for the standard deviation and mean.
Operations
StandardScaler is a Transformer. As such, it supports the fit and transform operation.
Fit
StandardScaler is trained on all subtypes of Vector or LabeledVector:
fit[T <: Vector]: DataSet[T] => Unitfit: DataSet[LabeledVector] => Unit
Transform
StandardScaler transforms all subtypes of Vector or LabeledVector into the respective type:
transform[T <: Vector]: DataSet[T] => DataSet[T]transform: DataSet[LabeledVector] => DataSet[LabeledVector]
Parameters
The standard scaler implementation can be controlled by the following two parameters:
| Parameters | Description |
|---|---|
| Mean | The mean of the scaled data set. (Default value: 0.0) |
| Std | The standard deviation of the scaled data set. (Default value: 1.0) |
Examples
// Create standard scaler transformer val scaler = StandardScaler()
.setMean(10.0)
.setStd(2.0)
// Obtain data set to be scaled val dataSet: DataSet[Vector] = ...
// Learn the mean and standard deviation of the training data scaler.fit(dataSet)
// Scale the provided data set to have mean=10.0 and std=2.0 val scaledDS = scaler.transform(dataSet)
