Question

I know that a linear regression model is generally evaluated using Adjusted R² or F value. How would you evaluate a logistic regression model?

Answer 1

In logistic regression, the model is evaluated using metrics such as deviance, area under the ROC curve (AUC), sensitivity, specificity, and precision.

Step-by-step explanation:

In logistic regression, the model is evaluated using different metrics than linear regression. One common metric is the deviance, which measures how well the model fits the data. The deviance is calculated by comparing the predicted probabilities from the logistic regression model to the actual binary outcomes.

Another metric used to evaluate the logistic regression model is the area under the ROC curve (AUC). The AUC measures the model's ability to distinguish between the two classes. Higher AUC values indicate better predictive performance.

Additionally, you can also consider metrics such as sensitivity, specificity, and precision to evaluate the performance of a logistic regression model, especially if the focus is on correctly classifying one particular class.