package sklearn

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package sklearn
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type tag = [
  1. | `RobustScaler
]
type t = [ `BaseEstimator | `Object | `RobustScaler | `TransformerMixin ] Obj.t
val of_pyobject : Py.Object.t -> t
val to_pyobject : [> tag ] Obj.t -> Py.Object.t
val as_transformer : t -> [ `TransformerMixin ] Obj.t
val as_estimator : t -> [ `BaseEstimator ] Obj.t
val create : ?with_centering:bool -> ?with_scaling:bool -> ?quantile_range:Py.Object.t -> ?copy:bool -> unit -> t

Scale features using statistics that are robust to outliers.

This Scaler removes the median and scales the data according to the quantile range (defaults to IQR: Interquartile Range). The IQR is the range between the 1st quartile (25th quantile) and the 3rd quartile (75th quantile).

Centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training set. Median and interquartile range 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. Typically this is done by removing the mean and scaling to unit variance. However, outliers can often influence the sample mean / variance in a negative way. In such cases, the median and the interquartile range often give better results.

.. versionadded:: 0.17

Read more in the :ref:`User Guide <preprocessing_scaler>`.

Parameters ---------- with_centering : boolean, True by default If True, center the data before scaling. This will cause ``transform`` to 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_scaling : boolean, True by default If True, scale the data to interquartile range.

quantile_range : tuple (q_min, q_max), 0.0 < q_min < q_max < 100.0 Default: (25.0, 75.0) = (1st quantile, 3rd quantile) = IQR Quantile range used to calculate ``scale_``.

.. versionadded:: 0.18

copy : boolean, optional, default is 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 ---------- center_ : array of floats The median value for each feature in the training set.

scale_ : array of floats The (scaled) interquartile range for each feature in the training set.

.. versionadded:: 0.17 *scale_* attribute.

Examples -------- >>> from sklearn.preprocessing import RobustScaler >>> X = [ 1., -2., 2.], ... [ -2., 1., 3.], ... [ 4., 1., -2.] >>> transformer = RobustScaler().fit(X) >>> transformer RobustScaler() >>> transformer.transform(X) array([ 0. , -2. , 0. ], [-1. , 0. , 0.4], [ 1. , 0. , -1.6])

See also -------- robust_scale: Equivalent function without the estimator API.

:class:`sklearn.decomposition.PCA` Further removes the linear correlation across features with 'whiten=True'.

Notes ----- 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>`.

https://en.wikipedia.org/wiki/Median https://en.wikipedia.org/wiki/Interquartile_range

val fit : ?y:Py.Object.t -> x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> t

Compute the median and quantiles to be used for scaling.

Parameters ---------- X : array-like, shape n_samples, n_features The data used to compute the median and quantiles used for later scaling along the features axis.

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.t

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 : 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.

val get_params : ?deep:bool -> [> tag ] Obj.t -> Dict.t

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 : x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> Py.Object.t

Scale back the data to the original representation

Parameters ---------- X : array-like The data used to scale along the specified axis.

val set_params : ?params:(string * Py.Object.t) list -> [> tag ] Obj.t -> t

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.

Parameters ---------- **params : dict Estimator parameters.

Returns ------- self : object Estimator instance.

val transform : x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> [> `ArrayLike ] Np.Obj.t

Center and scale the data.

Parameters ---------- X : array-like, sparse matrix The data used to scale along the specified axis.

val center_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute center_: get value or raise Not_found if None.

val center_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute center_: get value as an option.

val scale_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute scale_: get value or raise Not_found if None.

val scale_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute scale_: get value as an option.

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Stdlib.Format.formatter -> t -> unit

Pretty-print the object to a formatter.