The employment of Machine Learning (ML) models across diverse domains has grown exponentially in recent years. These models undertake critical tasks spanning medical diagnoses, criminal sentencing, and loan approvals. To enable users to grasp the rationale behind predictions and engender trust, these models should be interpretable. Equally vital is the capability of developers to pinpoint and rectify any erroneous behaviors. In this context emerges the field of Explainable Artificial Intelligence (XAI), which aims to develop methods to make ML models more interpretable while maintaining their performance level. Various methods have been proposed, many leveraging visual explanations to elucidate model behavior. However, a notable gap remains: a lack of rigorous assessment regarding the effectiveness of these explanations in enhancing i nterpretability. Previous findings showed that the visualizations presented by these methods can be confusing even for users who have a mathematical background and that there is a need for XAI researchers to work collaboratively with Information Visualization experts to develop these visualizations, as well as test the visualizations with users of various backgrounds. One of the most used XAI methods recently is the SHAP method, whose visual representations have not had their efficacy assessed before. Therefore, we developed a study where we worked together with visualization researchers and developed visualizations based on the information that the SHAP method provides, having in mind factors that are considered in literature to engender effectiveness to an explanation. We evaluated these visualizations with people from various backgrounds in order to assess if the visualizations are efficient in improving their understanding of the model. With the results of this study we propose an approach to produce and evaluate visual representations of explanations targeting their effectiveness.