Machine learning has started playing a significant role in complex decision-making processes. Accelerating this, counterfactual explanations in model explainability gives a brief idea about “what could have been the possible output if the input to the model had been changed in a particular way”. With this, one can understand user behaviour and for any recommendation or user conversion these explanations are very useful to get an estimate of which variable needs to be changed and by how much. Directive explanations for counterfactuals output provide the actions that are required for changing the input variable from state A to state B. These explanations are personalized in accordance with each user that makes this idea very unique. This paper includes a novel approach to get the directive explanations by using Markov Decision Processes and Reinforcement Learning.
