  Research in machine learning has successfully developed algorithms to build
accurate classification models. However, in many real-world applications, such
as healthcare, customer satisfaction, and environment protection, we want to be
able to use the models to decide what actions to take.
  We investigate the concept of actionability in the context of Support Vector
Machines. Actionability is as important as interpretability or explainability
of machine learning models, an ongoing and important research topic.
Actionability is the task that gives us ways to act upon machine learning
models and their predictions.
  This paper finds a solution to the question of actionability on both linear
and non-linear SVM models. Additionally, we introduce a way to account for
weighted actions that allow for more change in certain features than others. We
propose a gradient descent solution on the linear, RBF, and polynomial kernels,
and we test the effectiveness of our models on both synthetic and real
datasets. We are also able to explore the model's interpretability through the
lens of actionability.

Machine Learning;Artificial Intelligence