You are working on a spam classification system using regularized logistic regression. "Spam" is a positive class (y = 1) and "not spam" is the negative class (y = 0). You have trained your classifier and there are m = 1000 examples in the cross-validation set. The chart of predicted class vs. actual class is:

For reference:

• Accuracy = (true positives + true negatives) / (total examples)
• Precision = (true positives) / (true positives + false positives)
• Recall = (true positives) / (true positives + false negatives)
• F1score = (2 * precision * recall) / (precision + recall)

What is the classifier's recall (as a value from 0 to 1)?

Enter your answer in the box below. If necessary, provide at least two values after the decimal point.