MinMax Scaler
Description
The MinMax scaler scales the given data set, so that all values will lie between a user specified range [min,max]. In case the user does not provide a specific minimum and maximum value for the scaling range, the MinMax scaler transforms the features of the input data set to lie in the [0,1] interval. Given a set of input data \(x_1, x_2,… x_n\), with minimum value:
and maximum value:
The scaled data set \(z_1, z_2,…,z_n\) will be:
where \(\textit{min}\) and \(\textit{max}\) are the user specified minimum and maximum values of the range to scale.
Operations
MinMaxScaler is a Transformer. As such, it supports the fit and transform operation.
Fit
MinMaxScaler is trained on all subtypes of Vector or LabeledVector:
fit[T <: Vector]: DataSet[T] => Unitfit: DataSet[LabeledVector] => Unit
Transform
MinMaxScaler 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 MinMax scaler implementation can be controlled by the following two parameters:
| Parameters | Description |
|---|---|
| Min | The minimum value of the range for the scaled data set. (Default value: 0.0) |
| Max | The maximum value of the range for the scaled data set. (Default value: 1.0) |
Examples
// Create MinMax scaler transformer val minMaxscaler = MinMaxScaler()
.setMin(-1.0)
// Obtain data set to be scaled val dataSet: DataSet[Vector] = ...
// Learn the minimum and maximum values of the training data minMaxscaler.fit(dataSet)
// Scale the provided data set to have min=-1.0 and max=1.0 val scaledDS = minMaxscaler.transform(dataSet)
