标准化数据-StandardScaler

StandardScaler----计算训练集的平均值和标准差，以便测试数据集使用相同的变换

官方文档：

class sklearn.preprocessing.StandardScaler(copy=True, with_mean=True, with_std=True)

Standardize features by removing the mean and scaling to unit variance

通过删除平均值和缩放到单位方差来标准化特征

The standard score of a sample x is calculated as:

样本x的标准分数计算如下：

z = (x - u) / s

　　where u is the mean of the training samples or zero if with_mean=False, and s is the standard deviation of the training samples or one if with_std=False.

　　其中u是训练样本的均值，如果with_mean=False,则为0

　　s是训练样本的标准偏差，如果with_std=False,则为1

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.

See also

scale

Equivalent function without the estimator API.

sklearn.decomposition.PCA

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

Notes

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.

Examples

>>>

>>> 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.]]

Methods方法

__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 Ignored

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.

使用可选参数fit_params是变换器适合X和Y，并返回X的变换版本

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. copy : bool, optional (default: None) Copy the input X or not.
Returns:	X_tr : array-like, shape [n_samples, n_features] Transformed array.

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 Ignored

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

transform(X, y=’deprecated’, 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. y : (ignored) Deprecated since version 0.19: This parameter will be removed in 0.21. copy : bool, optional (default: None) Copy the input X or not.

原文地址：https://www.cnblogs.com/cola-1998/p/10218276.html