  Counterfactual explanations (CFE) are methods that explain a machine learning
model by giving an alternate class prediction of a data point with some minimal
changes in its features. It helps the users to identify their data attributes
that caused an undesirable prediction like a loan or credit card rejection. We
describe an efficient and an actionable counterfactual (CF) generation method
based on particle swarm optimization (PSO). We propose a simple objective
function for the optimization of the instance-centric CF generation problem.
The PSO brings in a lot of flexibility in terms of carrying out multi-objective
optimization in large dimensions, capability for multiple CF generation, and
setting box constraints or immutability of data attributes. An algorithm is
proposed that incorporates these features and it enables greater control over
the proximity and sparsity properties over the generated CFs. The proposed
algorithm is evaluated with a set of action-ability metrics in real-world
datasets, and the results were superior compared to that of the
state-of-the-arts.
