sklearn.preprocessing
.Binarizer¶
- class sklearn.preprocessing.Binarizer(*, threshold=0.0, copy=True)[source]¶
Binarize 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 User Guide.
- Parameters:
- thresholdfloat, default=0.0
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.
- copybool, 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).
- Attributes:
See also
binarize
Equivalent function without the estimator API.
KBinsDiscretizer
Bin continuous data into intervals.
OneHotEncoder
Encode categorical features as a one-hot numeric array.
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.
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.]])
Methods
fit
(X[, y])Do nothing and return the estimator unchanged.
fit_transform
(X[, y])Fit to data, then transform it.
get_feature_names_out
([input_features])Get output feature names for transformation.
get_params
([deep])Get parameters for this estimator.
set_params
(**params)Set the parameters of this estimator.
transform
(X[, copy])Binarize each element of X.
- fit(X, y=None)[source]¶
Do 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, sparse matrix} of shape (n_samples, n_features)
The data.
- yNone
Ignored.
- Returns:
- selfobject
Fitted transformer.
- fit_transform(X, y=None, **fit_params)[source]¶
Fit to data, then transform it.
Fits transformer to
X
andy
with optional parametersfit_params
and returns a transformed version ofX
.- 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.
If
input_features
isNone
, thenfeature_names_in_
is used as feature names in. Iffeature_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, theninput_features
must matchfeature_names_in_
iffeature_names_in_
is defined.
- Returns:
- feature_names_outndarray of str objects
Same as input features.
- 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.
- 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.
- transform(X, copy=None)[source]¶
Binarize each element of X.
- Parameters:
- X{array-like, sparse matrix} of 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.
- copybool
Copy the input X or not.
- Returns:
- X_tr{ndarray, sparse matrix} of shape (n_samples, n_features)
Transformed array.