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).      See also  
binarize


Equivalent function without the estimator API.    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_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 and y with optional parameters fit_params and returns a transformed version of X.  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_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.