type t = [ `BaseEstimator | `Binarizer | `Object | `TransformerMixin ] Obj.tval of_pyobject : Py.Object.t -> tval to_pyobject : [> tag ] Obj.t -> Py.Object.tval create : ?threshold:float -> ?copy:bool -> unit -> tBinarize data (set feature values to 0 or 1) according to a threshold
Values greater than the threshold map to 1, while values less than or equal to the threshold map to 0. With the default threshold of 0, only positive values map to 1.
Binarization is a common operation on text count data where the analyst can decide to only consider the presence or absence of a feature rather than a quantified number of occurrences for instance.
It can also be used as a pre-processing step for estimators that consider boolean random variables (e.g. modelled using the Bernoulli distribution in a Bayesian setting).
Read more in the :ref:`User Guide <preprocessing_binarization>`.
Parameters ---------- threshold : float, optional (0.0 by default) Feature values below or equal to this are replaced by 0, above it by 1. Threshold may not be less than 0 for operations on sparse matrices.
copy : boolean, optional, default True set to False to perform inplace binarization and avoid a copy (if the input is already a numpy array or a scipy.sparse CSR matrix).
Examples -------- >>> from sklearn.preprocessing import Binarizer >>> X = [ 1., -1., 2.],
... [ 2., 0., 0.],
... [ 0., 1., -1.] >>> transformer = Binarizer().fit(X) # fit does nothing. >>> transformer Binarizer() >>> transformer.transform(X) array([1., 0., 1.],
[1., 0., 0.],
[0., 1., 0.])
Notes ----- If the input is a sparse matrix, only the non-zero values are subject to update by the Binarizer class.
This estimator is stateless (besides constructor parameters), the fit method does nothing but is useful when used in a pipeline.
See also -------- binarize: Equivalent function without the estimator API.
val fit : ?y:Py.Object.t -> x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> tDo nothing and return the estimator unchanged
This method is just there to implement the usual API and hence work in pipelines.
Parameters ---------- X : array-like
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 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.tBinarize each element of X
Parameters ---------- X : array-like, sparse matrix, shape n_samples, n_features The data to binarize, element by element. scipy.sparse matrices should be in CSR format to avoid an un-necessary copy.
copy : bool Copy the input X or not.
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.