Voir la documentation de boxPlot
The boxPlot action calculates quantiles, high and low whiskers, and outliers for numeric variables. This action is essential for exploratory data analysis, allowing for a quick understanding of the distribution of data, its central tendency, variability, and the presence of outliers. It is widely used in statistics and data analysis to create box-and-whisker plots.
The `assess` action in the Percentile action set is a powerful tool for evaluating and comparing the performance of predictive models in SAS Viya. It is particularly useful in machine learning workflows to understand how well a model's predictions align with actual outcomes. This action can handle both classification (binary/nominal targets) and regression (interval targets) models. For classification, it computes essential metrics like ROC (Receiver Operating Characteristic) curves, lift charts, and various fit statistics (e.g., accuracy, misclassification rate). For regression, it calculates error metrics like Mean Squared Error (MSE). The action allows for detailed analysis by providing options to bin data, handle missing values, and partition data for validation, making it a cornerstone for robust model assessment.
La acción `percentile.boxPlot` en SAS Viya es una herramienta de análisis estadístico que se utiliza para calcular un conjunto completo de estadísticas descriptivas necesarias para construir diagramas de caja (box plots). Estos diagramas son fundamentales para visualizar la distribución de datos numéricos, identificar la mediana, los cuartiles, los valores atípicos y la dispersión de una variable. La acción puede procesar grandes volúmenes de datos de manera eficiente en el entorno distribuido de CAS.