preprocessing.StandardScaler()

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 feature 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:

with_mean : boolean, True by default If True, center the data before scaling. This does not work (and will raise an exception) when attempted on sparse matrices, because centering them entails building a dense matrix which in common use cases is likely to be too large to fit in memory. with_std : boolean, True by default If True, scale the data to unit variance (or equivalently, unit standard deviation). copy : boolean, optional, default True If False, try to avoid a copy and do inplace scaling instead. This is not guaranteed to always work inplace; e.g. if the data is not a NumPy array or scipy.sparse CSR matrix, a copy may still be returned.

Attributes:

scale_ : ndarray, shape (n_features,) Per feature relative scaling of the data. New in version 0.17: scale_ is recommended instead of deprecated std_. mean_ : array of floats with shape [n_features] The mean value for each feature in the training set. var_ : array of floats with shape [n_features] The variance for each feature in the training set. Used to compute scale_ n_samples_seen_ : int The number of samples processed by the estimator. Will be reset on new calls to fit, but increments across partial_fit calls.

See also

scale

Equivalent function without the object oriented API.

sklearn.decomposition.PCA

Further removes the linear correlation across features with ?whiten=True?.

Methods

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:	X : {array-like, sparse matrix}, shape [n_samples, n_features] The data used to compute the mean and standard deviation used for later scaling along the features axis. y: Passthrough for ``Pipeline`` compatibility. :

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:	X : numpy array of shape [n_samples, n_features] Training set. y : numpy array of shape [n_samples] Target values.
Returns:	X_new : numpy array of shape [n_samples, n_features_new] Transformed array.

get_params(deep=True) [source]

Get parameters for this estimator.

Parameters:	deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping of string to any Parameter names mapped to their values.

inverse_transform(X, copy=None) [source]

Scale back the data to the original representation

Parameters:	X : array-like, shape [n_samples, n_features] The data used to scale along the features axis.

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): 242-247:

Parameters:	X : {array-like, sparse matrix}, shape [n_samples, n_features] The data used to compute the mean and standard deviation used for later scaling along the features axis. y: Passthrough for ``Pipeline`` compatibility. :

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:	self :

std_

DEPRECATED: Attribute std_ will be removed in 0.19. Use scale_ instead

transform(X, y=None, copy=None) [source]

Perform standardization by centering and scaling

Parameters:	X : array-like, shape [n_samples, n_features] The data used to scale along the features axis.

Examples using sklearn.preprocessing.StandardScaler

Prediction Latency

Classifier comparison

Comparing different clustering algorithms on toy datasets

Demo of DBSCAN clustering algorithm

L1 Penalty and Sparsity in Logistic Regression

Sparse recovery: feature selection for sparse linear models

Varying regularization in Multi-layer Perceptron

Robust Scaling on Toy Data

RBF SVM parameters