minmax_scale#

cuml.preprocessing.minmax_scale(X, feature_range=(0, 1), *, axis=0, copy=True)[source]#

Transform features by scaling each feature to a given range.

This estimator scales and translates each feature individually such that it is in the given range on the training set, i.e. between zero and one.

The transformation is given by (when axis=0):

X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))
X_scaled = X_std * (max - min) + min

where min, max = feature_range.

The transformation is calculated as (when axis=0):

X_scaled = scale * X + min - X.min(axis=0) * scale
where scale = (max - min) / (X.max(axis=0) - X.min(axis=0))

This transformation is often used as an alternative to zero mean, unit variance scaling.

Parameters:

Xarray-like of shape (n_samples, n_features): The data.
feature_rangetuple (min, max), default=(0, 1): Desired range of transformed data.
axisint, default=0: Axis used to scale along. If 0, independently scale each feature, otherwise (if 1) scale each sample.
copybool, default=True: Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion.