Common metrics to find accuracy of linnear regression model and Logistic regression model?

Question

Accepted Answer

Common metrics for linear and logistic regression models are R-squared and confusion matrix respectively.

For linear regression model, common metric is R-squared which measures the proportion of the variance in the dependent variable that is predictable from the independent variables.
For logistic regression model, common metric is confusion matrix which includes metrics like accuracy, precision, recall, and F1 score to evaluate the performance of the model.
Accuracy is the proportion of correct predictions out of the total predictions made by the model.
Precision is the proportion of true positive predictions out of all positive predictions made by the model.
Recall is the proportion of true positive predictions out of all actual positive instances in the dataset.
F1 score is the harmonic mean of precision and recall, providing a balance between the two metrics.

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