StandardScaler (original) (raw)

class sklearn.preprocessing.StandardScaler(*, copy=True, with_mean=True, with_std=True)[source]#

Standardize features by removing the mean and scaling to unit variance.

The standard score of a sample x is calculated as:

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 ifwith_std=False.

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 usingtransform.

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 than others, it might dominate the objective function and make the estimator unable to learn from other features correctly as expected.

StandardScaler is sensitive to outliers, and the features may scale differently from each other in the presence of outliers. For an example visualization, refer to Compare StandardScaler with other scalers.

This scaler can also be applied to sparse CSR or CSC matrices by passingwith_mean=False to avoid breaking the sparsity structure of the data.

Read more in the User Guide.

Parameters:

copybool, 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.

with_meanbool, default=True

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_stdbool, default=True

If True, scale the data to unit variance (or equivalently, unit standard deviation).

Attributes:

**scale_**ndarray of shape (n_features,) or None

Per feature relative scaling of the data to achieve zero mean and unit variance. Generally this is calculated using np.sqrt(var_). If a variance is zero, we can’t achieve unit variance, and the data is left as-is, giving a scaling factor of 1. scale_ is equal to Nonewhen with_std=False.

Added in version 0.17: scale_

**mean_**ndarray of shape (n_features,) or None

The mean value for each feature in the training set. Equal to None when with_mean=False and with_std=False.

**var_**ndarray of shape (n_features,) or None

The variance for each feature in the training set. Used to computescale_. Equal to None when with_mean=False andwith_std=False.

**n_features_in_**int

Number of features seen during fit.

Added in version 0.24.

**feature_names_in_**ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when Xhas feature names that are all strings.

Added in version 1.0.

**n_samples_seen_**int or ndarray of shape (n_features,)

The number of samples processed by the estimator for each feature. If there are no missing samples, the n_samples_seen will be an integer, otherwise it will be an array of dtype int. Ifsample_weights are used it will be a float (if no missing data) or an array of dtype float that sums the weights seen so far. Will be reset on new calls to fit, but increments acrosspartial_fit calls.

See also

scale

Equivalent function without the estimator API.

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.

We use a biased estimator for the standard deviation, equivalent tonumpy.std(x, ddof=0). Note that the choice of ddof is unlikely to affect model performance.

Examples

from sklearn.preprocessing import StandardScaler data = [[0, 0], [0, 0], [1, 1], [1, 1]] scaler = StandardScaler() print(scaler.fit(data)) StandardScaler() 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=None, sample_weight=None)[source]#

Compute the mean and std to be used for later scaling.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features)

The data used to compute the mean and standard deviation used for later scaling along the features axis.

yNone

Ignored.

sample_weightarray-like of shape (n_samples,), default=None

Individual weights for each sample.

Added in version 0.24: parameter sample_weight support to StandardScaler.

Returns:

selfobject

Fitted scaler.

fit_transform(X, y=None, **fit_params)[source]#

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_paramsand returns a transformed version of X.

Parameters:

Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns:

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_feature_names_out(input_features=None)[source]#

Get output feature names for transformation.

Parameters:

input_featuresarray-like of str or None, default=None

Input features.

Returns:

feature_names_outndarray of str objects

Same as input features.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

inverse_transform(X, copy=None)[source]#

Scale back the data to the original representation.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features)

The data used to scale along the features axis.

copybool, default=None

Copy the input X or not.

Returns:

X_tr{ndarray, sparse matrix} of shape (n_samples, n_features)

Transformed array.

partial_fit(X, y=None, sample_weight=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 ofn_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} of shape (n_samples, n_features)

The data used to compute the mean and standard deviation used for later scaling along the features axis.

yNone

Ignored.

sample_weightarray-like of shape (n_samples,), default=None

Individual weights for each sample.

Added in version 0.24: parameter sample_weight support to StandardScaler.

Returns:

selfobject

Fitted scaler.

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → StandardScaler[source]#

Request metadata passed to the fit method.

Note that this method is only relevant ifenable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside aPipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in fit.

Returns:

selfobject

The updated object.

set_inverse_transform_request(*, copy: bool | None | str = '$UNCHANGED$') → StandardScaler[source]#

Request metadata passed to the inverse_transform method.

Note that this method is only relevant ifenable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside aPipeline. Otherwise it has no effect.

Parameters:

copystr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for copy parameter in inverse_transform.

Returns:

selfobject

The updated object.

set_output(*, transform=None)[source]#

Set output container.

See Introducing the set_output APIfor an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

Added in version 1.4: "polars" option was added.

Returns:

selfestimator instance

Estimator instance.

set_params(**params)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

set_partial_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → StandardScaler[source]#

Request metadata passed to the partial_fit method.

Note that this method is only relevant ifenable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside aPipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in partial_fit.

Returns:

selfobject

The updated object.

set_transform_request(*, copy: bool | None | str = '$UNCHANGED$') → StandardScaler[source]#

Request metadata passed to the transform method.

Note that this method is only relevant ifenable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside aPipeline. Otherwise it has no effect.

Parameters:

copystr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for copy parameter in transform.

Returns:

selfobject

The updated object.

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

Perform standardization by centering and scaling.

Parameters:

X{array-like, sparse matrix of shape (n_samples, n_features)

The data used to scale along the features axis.

copybool, default=None

Copy the input X or not.

Returns:

X_tr{ndarray, sparse matrix} of shape (n_samples, n_features)

Transformed array.