minmax_scale#

sklearn.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 suchthat it is in the given range on the training set, i.e. betweenzero and one.

The transformation is given by (whenaxis=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 (whenaxis=0):

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

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

Read more in theUser Guide.

Added in version 0.17:minmax_scale function interfacetoMinMaxScaler.

Parameters:

Xarray-like of shape (n_samples, n_features)

The data.

feature_rangetuple (min, max), default=(0, 1)

Desired range of transformed data.

axis{0, 1}, default=0

Axis used to scale along. If 0, independently scale each feature,otherwise (if 1) scale each sample.

copybool, default=True

If False, try to avoid a copy and scale in place.This is not guaranteed to always work in place; e.g. if the data isa numpy array with an int dtype, a copy will be returned even withcopy=False.

Returns:

X_trndarray of shape (n_samples, n_features)

The transformed data.

Warning

Risk of data leakDo not useminmax_scale unless you knowwhat you are doing. A common mistake is to apply it to the entire databefore splitting into training and test sets. This will bias themodel evaluation because information would have leaked from the testset to the training set.In general, we recommend usingMinMaxScaler within aPipeline in order to prevent most risks of dataleaking:pipe=make_pipeline(MinMaxScaler(),LogisticRegression()).

See also

MinMaxScaler

Performs scaling to a given range using the Transformer API (e.g. as part of a preprocessingPipeline).

Notes

For a comparison of the different scalers, transformers, and normalizers,see:Compare the effect of different scalers on data with outliers.

Examples

>>>fromsklearn.preprocessingimportminmax_scale>>>X=[[-2,1,2],[-1,0,1]]>>>minmax_scale(X,axis=0)# scale each column independentlyarray([[0., 1., 1.],       [1., 0., 0.]])>>>minmax_scale(X,axis=1)# scale each row independentlyarray([[0.  , 0.75, 1.  ],       [0.  , 0.5 , 1.  ]])

Gallery examples#

Restricted Boltzmann Machine features for digit classification

Restricted Boltzmann Machine features for digit classification

Compare the effect of different scalers on data with outliers

Compare the effect of different scalers on data with outliers