Logistic Regression - Model Accuracy

Model Accuracy

A way to test for errors in models created by step-wise regression is to not rely on the model's F-statistic, significance, or multiple-r, but instead assess the model against a set of data that was not used to create the model. The class of techniques is called cross-validation.

Accuracy is measured as correctly classified records in the holdout sample. There are four possible classifications:

1. prediction of 0 when the holdout sample has a 0 (True Negative/TN)
2. prediction of 0 when the holdout sample has a 1 (False Negative/FN)
3. prediction of 1 when the holdout sample has a 0 (False Positive/FP)
4. prediction of 1 when the holdout sample has a 1 (True Positive/TP)

These classifications are used to measure Precision and Recall:

The percent of correctly classified observations in the holdout sample is referred to the assessed model accuracy. Additional accuracy can be expressed as the model's ability to correctly classify 0, or the ability to correctly classify 1 in the holdout dataset. The holdout model assessment method is particularly valuable when data are collected in different settings (e.g., at different times or places) or when models are assumed to be generalizable.