What is a confusion matrix in predictive analytics, and how is it used to evaluate model performance? What are the key components such as true positives, true negatives, false positives, and false negatives? How does a confusion matrix help in calculating metrics like accuracy, precision, recall, and F1-score? In what types of predictive models is a confusion matrix commonly applied? What are the advantages and limitations of using a confusion matrix for model evaluation?