AP BIOLOGY

Detailed explanation-1: -A true positive is an outcome where the model correctly predicts the positive class. Similarly, a true negative is an outcome where the model correctly predicts the negative class. A false positive is an outcome where the model incorrectly predicts the positive class.

Detailed explanation-2: -Confusion matrix visualization. True positive (TP): Observation is predicted positive and is actually positive. False positive (FP): Observation is predicted positive and is actually negative. True negative (TN): Observation is predicted negative and is actually negative.

Detailed explanation-3: -A true positive is where a rule is configured and logs match that rule for a real threat. This means the rule worked as intended and alarmed correctly. A false positive is where a rule is configured and the log matches the rule, however the logs that matched are not considered a threat and should be ignored.

Detailed explanation-4: -Accuracy is the proportion of true results, either true positive or true negative, in a population. It measures the degree of veracity of a diagnostic test on a condition. The numerical values of sensitivity represents the probability of a diagnostic test identifies patients who do in fact have the disease.