AddDummyFeature insert a column of ones to fit intercept.

DenseMean puts in Xmean[1,nFeatures] the mean of X rows.

IncrementalMeanAndVar Calculate mean update and a Youngs and Cramer variance update.

Mean for mat.Matrix.

MeanStdDev for mat.Matrix.

NewBinarizer ...

NewDefaultRobustScaler supplies typical arguments (via python sklearn).

NewFunctionTransformer ...

NewImputer ...

NewKBinsDiscretizer returns a discretizer with Encode="onehot-dense" ans strategy="quantile".

NewKernelCenterer ...

NewLabelBinarizer ...

NewLabelEncoder ...

NewMaxAbsScaler ...

NewMinMaxScaler creates an *MinMaxScaler with FeatureRange 0..1.

NewMultiLabelBinarizer ...

NewNormalizer returns a normaliser with Norm l2 and axis 1.

NewOneHotEncoder creates a *OneHotEncoder.

NewPCA returns a *PCA.

NewPolynomialFeatures creates a *PolynomialFeatures.

NewPowerTransformer returns a PowerTransformer with method yeo-johnson and standardize=true.

NewQuantileTransformer returns a new QuantileTransformer.

NewRobustScaler creates a *RobustScaler.

NewShuffler returns a *Shuffler.

NewStandardScaler creates a *StandardScaler.

Scale provides a quick and easy way to perform this operation on a single array-like dataset.

Binarizer Binarize data (set feature values to 0 or 1) according to a threshold.

FunctionTransformer Constructs a transformer from an arbitrary callable.

Imputer ..

KBinsDiscretizer structure Encode = "onehot-dense","ordinal" Strategy = "quantile","uniform","kmeans".

KernelCenterer Center a kernel matrix.

LabelBinarizer Binarize labels in a one-vs-all fashion.

LabelEncoder Encode labels with value between 0 and n_classes-1.

MaxAbsScaler ...

MinMaxScaler rescale data between FeatureRange.

MultiLabelBinarizer Transform between iterable of iterables and a multilabel format.

Normalizer Normalize samples individually to unit norm.

NumpyLike is a namespace for numpy-like var and std.

OneHotEncoder Encode categorical integer features using a one-hot aka one-of-K scheme.

PCA is a thin single value decomposition transformer.

PolynomialFeatures struct.

PowerTransformer apply a power transform featurewise to make data more Gaussian-like TODO support boxcox.

QuantilePair represents bounds of quantile.

QuantileTransformer Transform features using quantiles information.

RobustScaler scales data by removing centering around the Median and removing outliers by Quantile.

Shuffler shuffles rows of X and Y.

StandardScaler scales data by removing Mean and dividing by stddev.

InverseTransformer is a transformer able to inverse his tranformation.

Transformer is an interface for various preprocessors.