標準化數據-StandardScaler
StandardScaler----計算訓練集的平均值和標準差,以便測試數據集使用相同的變換
官方文檔:
class sklearn.preprocessing.
StandardScaler
(copy=True, with_mean=True, with_std=True)
Standardize features by removing the mean and scaling to unit variance
通過刪除平均值和縮放到單位方差來標準化特征
The standard score of a sample x is calculated as:
樣本x的標準分數計算如下:
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是訓練樣本的均值,如果with_mean=False,則為0
s是訓練樣本的標準偏差,如果with_std=False,則為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
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
- 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
fit (X[, y]) |
Compute the mean and std to be used for later scaling. 計算用於以後縮放的mean和std |
fit_transform (X[, y]) |
Fit to data, then transform it. 適合數據,然後轉換它 |
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 通過居中和縮放執行標準化 |
__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.
使用可選參數fit_params是變換器適合X和Y,並返回X的變換版本
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.
標準化數據-StandardScaler