class StandardScaler extends Logging
Standardizes features by removing the mean and scaling to unit std using column summary statistics on the samples in the training set.
The "unit std" is computed using the corrected sample standard deviation (https://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation), which is computed as the square root of the unbiased sample variance.
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StandardScaler()
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new
StandardScaler(withMean: Boolean, withStd: Boolean)
- withMean
False by default. Centers the data with mean before scaling. It will build a dense output, so take care when applying to sparse input.
- withStd
True by default. Scales the data to unit standard deviation.
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def
fit(data: RDD[Vector]): StandardScalerModel
Computes the mean and variance and stores as a model to be used for later scaling.
Computes the mean and variance and stores as a model to be used for later scaling.
- data
The data used to compute the mean and variance to build the transformation model.
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a StandardScalarModel
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