Which node helps in visualizing relationships between variables in a dataset?

The distribution node is specifically designed to visualize the distribution of a single variable in a dataset, typically showing how data points are spread across different values. It can highlight outliers, skewness, and the overall shape of the distribution, but it does not directly compare relationships between multiple variables.

On the other hand, the matrix node offers a more comprehensive view of interactions between two or more variables, providing a means to visualize and analyze the relationships between these variables. For instance, it can showcase correlations, dependencies, or any significant patterns that emerge when different variables are plotted against each other. This feature makes the matrix node particularly useful for understanding multi-variate interactions in your data, which is a key aspect of exploring relationships in predictive analytics.

Therefore, the matrix node serves as the correct answer for visualizing relationships between variables in a dataset, enabling the identification of potential associations or conflicts between the data points being analyzed.