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.

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 or CSC format to avoid an un-necessary copy.

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 / CSC matrix and if axis is 1).

X_tr{ndarray, sparse matrix} of shape (n_samples, n_features)

The transformed data.

See also


Performs binarization using the Transformer API (e.g. as part of a preprocessing Pipeline).