power_transform(X, method='warn', standardize=True, copy=True)¶
Power transforms are a family of parametric, monotonic transformations that are applied to make data more Gaussian-like. This is useful for modeling issues related to heteroscedasticity (non-constant variance), or other situations where normality is desired.
Currently, power_transform supports the Box-Cox transform and the Yeo-Johnson transform. The optimal parameter for stabilizing variance and minimizing skewness is estimated through maximum likelihood.
Box-Cox requires input data to be strictly positive, while Yeo-Johnson supports both positive or negative data.
By default, zero-mean, unit-variance normalization is applied to the transformed data.
Read more in the User Guide.
- Xarray-like, shape (n_samples, n_features)
The data to be transformed using a power transformation.
The power transform method. Available methods are:
‘yeo-johnson’ , works with positive and negative values
‘box-cox’ , only works with strictly positive values
The default method will be changed from ‘box-cox’ to ‘yeo-johnson’ in version 0.23. To suppress the FutureWarning, explicitly set the parameter.
- standardizeboolean, default=True
Set to True to apply zero-mean, unit-variance normalization to the transformed output.
- copyboolean, optional, default=True
Set to False to perform inplace computation during transformation.
- X_transarray-like, shape (n_samples, n_features)
The transformed data.
NaNs are treated as missing values: disregarded in
fit, and maintained in
For a comparison of the different scalers, transformers, and normalizers, see examples/preprocessing/plot_all_scaling.py.
I.K. Yeo and R.A. Johnson, “A new family of power transformations to improve normality or symmetry.” Biometrika, 87(4), pp.954-959, (2000).
G.E.P. Box and D.R. Cox, “An Analysis of Transformations”, Journal of the Royal Statistical Society B, 26, 211-252 (1964).
>>> import numpy as np >>> from sklearn.preprocessing import power_transform >>> data = [[1, 2], [3, 2], [4, 5]] >>> print(power_transform(data, method='box-cox')) [[-1.332... -0.707...] [ 0.256... -0.707...] [ 1.076... 1.414...]]