type t =
[ `BaseEstimator | `Object | `StandardScaler | `TransformerMixin ] Obj.tval of_pyobject : Py.Object.t -> tval to_pyobject : [> tag ] Obj.t -> Py.Object.tval create : ?copy:bool -> ?with_mean:bool -> ?with_std:bool -> unit -> tStandardize 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 :meth:`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 :ref:`User Guide <preprocessing_scaler>`.
Parameters ---------- 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.
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).
Attributes ---------- scale_ : ndarray or None, shape (n_features,) Per feature relative scaling of the data. This is calculated using `np.sqrt(var_)`. Equal to ``None`` when ``with_std=False``.
.. versionadded:: 0.17 *scale_*
mean_ : ndarray or None, shape (n_features,) The mean value for each feature in the training set. Equal to ``None`` when ``with_mean=False``.
var_ : ndarray or None, shape (n_features,) The variance for each feature in the training set. Used to compute `scale_`. Equal to ``None`` when ``with_std=False``.
n_samples_seen_ : int or array, shape (n_features,) 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.
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.]
See also -------- scale: Equivalent function without the estimator API.
:class:`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.
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.
For a comparison of the different scalers, transformers, and normalizers, see :ref:`examples/preprocessing/plot_all_scaling.py <sphx_glr_auto_examples_preprocessing_plot_all_scaling.py>`.
val fit : ?y:Py.Object.t -> x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> tCompute 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
val fit_transform :
?y:[> `ArrayLike ] Np.Obj.t ->
?fit_params:(string * Py.Object.t) list ->
x:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
[> `ArrayLike ] Np.Obj.tFit 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 : array-like, sparse matrix, dataframe of shape (n_samples, n_features)
y : ndarray of shape (n_samples,), default=None Target values.
**fit_params : dict Additional fit parameters.
Returns ------- X_new : ndarray array of shape (n_samples, n_features_new) Transformed array.
Get parameters for this estimator.
Parameters ---------- deep : bool, default=True 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.
val inverse_transform :
?copy:bool ->
x:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
[> `ArrayLike ] Np.Obj.tScale 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.
val partial_fit :
?y:Py.Object.t ->
x:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
tOnline 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 :meth:`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 : None Ignored.
Returns ------- self : object Transformer instance.
val set_params : ?params:(string * Py.Object.t) list -> [> tag ] Obj.t -> tSet 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.
Parameters ---------- **params : dict Estimator parameters.
Returns ------- self : object Estimator instance.
val transform :
?copy:bool ->
x:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
[> `ArrayLike ] Np.Obj.tPerform standardization by centering and scaling
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.
val n_samples_seen_ : t -> Py.Object.tAttribute n_samples_seen_: get value or raise Not_found if None.
val n_samples_seen_opt : t -> Py.Object.t optionAttribute n_samples_seen_: get value as an option.
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Format.formatter -> t -> unitPretty-print the object to a formatter.