modAL.expected_error¶
Expected error reduction framework for active learning.
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modAL.expected_error.
expected_error_reduction
(learner: modAL.models.learners.ActiveLearner, X: Union[list, numpy.ndarray, scipy.sparse.csr.csr_matrix], loss: str = 'binary', p_subsample: float = 1.0, n_instances: int = 1, random_tie_break: bool = False) → Tuple[numpy.ndarray, Union[list, numpy.ndarray, scipy.sparse.csr.csr_matrix]][source]¶ Expected error reduction query strategy.
References
Roy and McCallum, 2001 (http://groups.csail.mit.edu/rrg/papers/icml01.pdf)
Parameters: - learner – The ActiveLearner object for which the expected error is to be estimated.
- X – The samples.
- loss – The loss function to be used. Can be ‘binary’ or ‘log’.
- p_subsample – Probability of keeping a sample from the pool when calculating expected error. Significantly improves runtime for large sample pools.
- n_instances – The number of instances to be sampled.
- random_tie_break – If True, shuffles utility scores to randomize the order. This can be used to break the tie when the highest utility score is not unique.
Returns: The indices of the instances from X chosen to be labelled; the instances from X chosen to be labelled.