sklearn.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.
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: mean_ : array of floats with shape [n_features]
The mean value for each feature in the training set.
std_ : array of floats with shape [n_features]
The standard deviation for each feature in the training set.
See also
sklearn.preprocessing.scale, scaling, sklearn.decomposition.RandomizedPCA, to
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 set_params(**params) Set the parameters of this estimator. transform(X[, y, copy]) Perform standardization by centering and scaling - fit(X, y=None)[source]¶
Compute the mean and std to be used for later scaling.
Parameters: X : array-like or CSR matrix with shape [n_samples, n_features]
The data used to compute the mean and standard deviation used for later scaling along the features axis.
- 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 with shape [n_samples, n_features]
The data used to scale along the features axis.
- 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 former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns: self :