cuml.preprocessing

Binarizer	Binarize data (set feature values to 0 or 1) according to a threshold
FunctionTransformer	Constructs a transformer from an arbitrary callable.
KBinsDiscretizer	Bin continuous data into intervals.
KernelCenterer	Center a kernel matrix
LabelBinarizer	Binarize labels in a one-vs-all fashion.
LabelEncoder	Encode target labels with values between 0 and n_classes - 1.
MaxAbsScaler	Scale each feature by its maximum absolute value.
MinMaxScaler	Transform features by scaling each feature to a given range.
MissingIndicator	Binary indicators for missing values.
Normalizer	Normalize samples individually to unit norm.
OneHotEncoder	Encode categorical features as a one-hot numeric array.
PolynomialFeatures	Generate polynomial and interaction features.
PowerTransformer	Apply a power transform featurewise to make data more Gaussian-like.
QuantileTransformer	Transform features using quantiles information.
RobustScaler	Scale features using statistics that are robust to outliers.
SimpleImputer	Imputation transformer for completing missing values.
StandardScaler	Standardize features by removing the mean and scaling to unit variance
TargetEncoder	A cudf based implementation of target encoding [R331d970812b4-1], which converts one or multiple categorical variables, 'Xs', with the average of corresponding values of the target variable, 'Y'.
add_dummy_feature	Augment dataset with an additional dummy feature.
binarize	Boolean thresholding of array-like or sparse matrix
label_binarize	Binarize labels in a one-vs-all fashion.
maxabs_scale	Scale each feature to the [-1, 1] range without breaking the sparsity.
minmax_scale	Transform features by scaling each feature to a given range.
normalize	Scale input vectors individually to unit norm (vector length).
robust_scale	Standardize a dataset along any axis
scale	Standardize a dataset along any axis

Text Preprocessing

PorterStemmer

A word stemmer based on the Porter stemming algorithm.