Improving Decision Sparsity
This work addresses interpretability in machine learning for users affected by decisions, though it appears incremental as it builds on an existing notion of decision sparsity.
The paper tackled the problem of making sparsity more relevant to decision-making by expanding the Sparse Explanation Value (SEV) to derive sparser and more meaningful explanations for various function classes, resulting in improved credibility and optimized decision sparsity in models.
Sparsity is a central aspect of interpretability in machine learning. Typically, sparsity is measured in terms of the size of a model globally, such as the number of variables it uses. However, this notion of sparsity is not particularly relevant for decision-making; someone subjected to a decision does not care about variables that do not contribute to the decision. In this work, we dramatically expand a notion of decision sparsity called the Sparse Explanation Value(SEV) so that its explanations are more meaningful. SEV considers movement along a hypercube towards a reference point. By allowing flexibility in that reference and by considering how distances along the hypercube translate to distances in feature space, we can derive sparser and more meaningful explanations for various types of function classes. We present cluster-based SEV and its variant tree-based SEV, introduce a method that improves credibility of explanations, and propose algorithms that optimize decision sparsity in machine learning models.