End-to-End Data Science with SAS

Chapter 9: Model Evaluation Metrics

Overview

General Information

Data Source Statement

Data Dictionary

Variable Summaries

Model Output

Model Overview

Accuracy Statistics

Regression Models

Classification Models

Black-Box Evaluation Tools

Variable Importance

Partial Dependence (PD) Plots

Individual Conditional Expectation (ICE) Plots

Chapter Review

Overview

Model evaluation is essential for determining the accuracy and effectiveness of your model. Evaluation metrics can help a data scientist develop a robust model on the training and validation data sets while also providing a framework to evaluate the accuracy of the final model by applying these metrics to the hold-out test and out-of-time data sets. A data scientist must select the appropriate ...