What feature in SPSS Modeler aids in the representation of data distributions and outliers?

The Histogram node in SPSS Modeler is specifically designed to visualize data distributions, making it a valuable tool for understanding the characteristics of data. By creating a histogram, you can see how data points are spread across different ranges, revealing patterns such as skewness, modality (number of peaks), and the presence of outliers.

Histograms effectively display the frequency distribution of continuous variables, which allows analysts to quickly inspect how data is grouped and identify any observations that fall significantly outside the typical range. This visual clarity helps in making informed decisions for data preprocessing, feature engineering, and subsequent analysis.

In contrast, other nodes like the Cluster node focus on grouping similar observations into clusters, the Transformation node is used for modifying or transforming data, and the Data Audit node provides summary statistics and data quality assessments but does not primarily visualize distributions in the same direct and intuitive manner that histograms do. Thus, the Histogram node is the most fitting choice for representing data distributions and identifying outliers.