class sklearn.preprocessing.StandardScaler(copy=True, with_mean=True, with_std=True)
[source]
Standardize features by removing the mean and scaling to unit variance
Centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training set. Mean and standard deviation are then stored to be used on later data using the transform
method.
Standardization of a dataset is a common requirement for many machine learning estimators: they might behave badly if the individual features do not more or less look like standard normally distributed data (e.g. Gaussian with 0 mean and unit variance).
For instance many elements used in the objective function of a learning algorithm (such as the RBF kernel of Support Vector Machines or the L1 and L2 regularizers of linear models) assume that all features are centered around 0 and have variance in the same order. If a feature has a variance that is orders of magnitude larger that others, it might dominate the objective function and make the estimator unable to learn from other features correctly as expected.
This scaler can also be applied to sparse CSR or CSC matrices by passing with_mean=False
to avoid breaking the sparsity structure of the data.
Read more in the User Guide.
Parameters: 


Attributes: 

See also
scale
sklearn.decomposition.PCA
NaNs are treated as missing values: disregarded in fit, and maintained in transform.
For a comparison of the different scalers, transformers, and normalizers, see examples/preprocessing/plot_all_scaling.py.
>>> from sklearn.preprocessing import StandardScaler >>> data = [[0, 0], [0, 0], [1, 1], [1, 1]] >>> scaler = StandardScaler() >>> print(scaler.fit(data)) StandardScaler(copy=True, with_mean=True, with_std=True) >>> print(scaler.mean_) [0.5 0.5] >>> print(scaler.transform(data)) [[1. 1.] [1. 1.] [ 1. 1.] [ 1. 1.]] >>> print(scaler.transform([[2, 2]])) [[3. 3.]]
fit (X[, y])  Compute the mean and std to be used for later scaling. 
fit_transform (X[, y])  Fit to data, then transform it. 
get_params ([deep])  Get parameters for this estimator. 
inverse_transform (X[, copy])  Scale back the data to the original representation 
partial_fit (X[, y])  Online computation of mean and std on X for later scaling. 
set_params (**params)  Set the parameters of this estimator. 
transform (X[, y, copy])  Perform standardization by centering and scaling 
__init__(copy=True, with_mean=True, with_std=True)
[source]
fit(X, y=None)
[source]
Compute the mean and std to be used for later scaling.
Parameters: 


fit_transform(X, y=None, **fit_params)
[source]
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
Parameters: 


Returns: 

get_params(deep=True)
[source]
Get parameters for this estimator.
Parameters: 


Returns: 

inverse_transform(X, copy=None)
[source]
Scale back the data to the original representation
Parameters: 


Returns: 

partial_fit(X, y=None)
[source]
Online computation of mean and std on X for later scaling. All of X is processed as a single batch. This is intended for cases when fit
is not feasible due to very large number of n_samples
or because X is read from a continuous stream.
The algorithm for incremental mean and std is given in Equation 1.5a,b in Chan, Tony F., Gene H. Golub, and Randall J. LeVeque. “Algorithms for computing the sample variance: Analysis and recommendations.” The American Statistician 37.3 (1983): 242247:
Parameters: 


set_params(**params)
[source]
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.
Returns: 


transform(X, y=’deprecated’, copy=None)
[source]
Perform standardization by centering and scaling
Parameters: 


sklearn.preprocessing.StandardScaler
© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html