Binarizer¶
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).
Configuration:
threshold
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.
Attributes:
Input ports:
- Output ports:
- model : model
- Model
- threshold (threshold)
- 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.
Some of the docstrings for this module have been automatically extracted from the scikit-learn library and are covered by their respective licenses.
