Voir la documentation de assess
La acción `assess` del conjunto de acciones `percentile` se utiliza para evaluar y comparar el rendimiento de modelos predictivos. Es especialmente útil para modelos de clasificación, generando métricas como la curva ROC, la tabla de elevación (lift chart) y estadísticas de ajuste para determinar la precisión y eficacia del modelo.
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.