# StandardScaler

### *class* cuml.preprocessing.StandardScaler(\*args, \*\*kwargs)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/_thirdparty/sklearn/preprocessing/_data.py#L527)

Standardize features by removing the mean and scaling to unit variance

The standard score of a sample `x` is calculated as:

> 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`.

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
[`transform()`](#cuml.preprocessing.StandardScaler.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.

* **Parameters:**
  **copy**
  : Whether a forced copy will be triggered. If copy=False, a copy might
    be triggered by a conversion.

  **with_mean**
  : 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**
  : If True, scale the data to unit variance (or equivalently,
    unit standard deviation).
* **Attributes:**
  **scale_**
  : Per feature relative scaling of the data. This is calculated using
    `sqrt(var_)`. Equal to `None` when `with_std=False`.

  **mean_**
  : The mean value for each feature in the training set.
    Equal to `None` when `with_mean=False`.

  **var_**
  : The variance for each feature in the training set. Used to compute
    `scale_`. Equal to `None` when `with_std=False`.

  **n_samples_seen_**
  : The number of samples processed by the estimator for each feature.
    If there are not missing samples, the `n_samples_seen` will be an
    integer, otherwise it will be an array.
    Will be reset on new calls to fit, but increments across
    `partial_fit` calls.

### Methods

| [`fit`](#cuml.preprocessing.StandardScaler.fit)(X[, y])                  | Compute the mean and std to be used for later scaling.     |
|-------------------------------------------------------------------------------|------------------------------------------------------------|
| [`inverse_transform`](#cuml.preprocessing.StandardScaler.inverse_transform)(X[, copy]) | Scale back the data to the original representation         |
| [`partial_fit`](#cuml.preprocessing.StandardScaler.partial_fit)(X[, y])          | Online computation of mean and std on X for later scaling. |
| [`transform`](#cuml.preprocessing.StandardScaler.transform)(X[, copy])         | Perform standardization by centering and scaling           |

#### SEE ALSO
[`scale`](../cuml.preprocessing.scale/index.html.md#cuml.preprocessing.scale)
: Equivalent function without the estimator API.

[`cuml.decomposition.PCA`](../cuml.decomposition.PCA/index.html.md#cuml.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.

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

### Examples

```pycon
>>> from cuml.preprocessing import StandardScaler
>>> import cupy as cp
>>> data = [[0, 0], [0, 0], [1, 1], [1, 1]]
>>> data = cp.array(data)
>>> 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(cp.array([[2, 2]])))
[[3. 3.]]
```

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#### as_sklearn()[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/internals/interop.py#L206)

Convert this estimator into an equivalent scikit-learn (or scikit-learn
extension) estimator.

* **Returns:**
  sklearn.base.BaseEstimator
  : A scikit-learn compatible estimator instance that mirrors the trained
    state of the current estimator.

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#### fit(X, y=None) → [StandardScaler](#cuml.preprocessing.StandardScaler)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/_thirdparty/sklearn/preprocessing/_data.py#L720)

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

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

  **y**
  : Ignored

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#### fit_transform(X, y=None, \*\*fit_params)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/_thirdparty/sklearn/utils/skl_dependencies.py#L42)

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**

  **y**
  : Target values.

  **\*\*fit_params**
  : Additional fit parameters.
* **Returns:**
  **X_new**
  : Transformed array.

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#### *classmethod* from_sklearn(model)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/internals/interop.py#L222)

Create a cuml estimator from a scikit-learn estimator.

* **Parameters:**
  **model**
  : A compatible scikit-learn (or scikit-learn extension) estimator.
* **Returns:**
  cls
  : A new instance of this cuml estimator class that mirrors the
    state of the input estimator.

### Notes

`output_type` of the estimator is set to “numpy” by default, as these
cannot be inferred from training arguments. If something different is
required, then please use cuml’s output_type configuration utilities.

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#### get_feature_names_out(input_features=None)

Get output feature names for transformation.

* **Parameters:**
  **input_features**
  : Input features.
    - If `input_features` is [`None`](https://docs.python.org/3/builtins/constants.html#None), then `feature_names_in_` is
      used as feature names in. If `feature_names_in_` is not defined,
      then the following input feature names are generated:
      `["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
    - If `input_features` is an array-like, then `input_features` must
      match `feature_names_in_` if `feature_names_in_` is defined.
* **Returns:**
  **feature_names_out**
  : Same as input features.

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#### get_params(deep=True)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/internals/base.py#L191)

Returns a dict of all params owned by this class. If the child class
has appropriately overridden the `_get_param_names` method and does not
need anything other than what is there in this method, then it doesn’t
have to override this method

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#### inverse_transform(X, copy=None)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/_thirdparty/sklearn/preprocessing/_data.py#L905)

Scale back the data to the original representation

* **Parameters:**
  **X**
  : The data used to scale along the features axis.

  **copy**
  : Whether a forced copy will be triggered. If copy=False,
    a copy might be triggered by a conversion.
* **Returns:**
  **X_tr**
  : Transformed array.

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#### partial_fit(X, y=None) → [StandardScaler](#cuml.preprocessing.StandardScaler)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/_thirdparty/sklearn/preprocessing/_data.py#L738)

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()`](#cuml.preprocessing.StandardScaler.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**
  : The data used to compute the mean and standard deviation
    used for later scaling along the features axis.

  **y**
  : Ignored.
* **Returns:**
  **self**
  : Transformer instance.

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#### set_params(\*\*params)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/internals/base.py#L200)

Accepts a dict of params and updates the corresponding ones owned by
this class. If the child class has appropriately overridden the
`_get_param_names` method and does not need anything other than what is,
there in this method, then it doesn’t have to override this method

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#### transform(X, copy=None)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/_thirdparty/sklearn/preprocessing/_data.py#L865)

Perform standardization by centering and scaling

* **Parameters:**
  **X**
  : The data used to scale along the features axis.

  **copy**
  : Whether a forced copy will be triggered. If copy=False,
    a copy might be triggered by a conversion.

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