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sklearn.preprocessing.binarize

sklearn.preprocessing.binarize(X, threshold=0.0, copy=True) [source]

Boolean thresholding of array-like or scipy.sparse matrix

Read more in the User Guide.

Parameters:

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 or CSC format to avoid an un-necessary copy. `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 / CSC matrix and if axis is 1).

X : {array-like, sparse matrix}, shape [n_samples, n_features]: The data to binarize, element by element. scipy.sparse matrices should be in CSR or CSC format to avoid an un-necessary copy.
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 / CSC matrix and if axis is 1).