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).
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).
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.
Do nothing and return the estimator unchanged
This method is just there to implement the usual API and hence work in pipelines.
fit_transform(X, y=None, **fit_params)¶
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
X : numpy array of shape [n_samples, n_features]
y : numpy array of shape [n_samples]
X_new : numpy array of shape [n_samples, n_features_new]
Get parameters for this estimator.
deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
params : mapping of string to any
Parameter names mapped to their values.
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form
<component>__<parameter>so that it’s possible to update each component of a nested object.
Returns: self :
transform(X, y=None, copy=None)¶
Binarize each element of X
X : array or scipy.sparse matrix with 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.