False Positive (FP) Classification Error

A False Positive (FP) Classification Error is a binary classification error where a negative instance is incorrectly classified as a positive instance.

• AKA: False Positive, FP Outcome, False Positive Prediction, False Positive Result, False Positive Error, Type I Classification Error.
• Context:
  • It can typically occur when a classification model incorrectly assigns the positive class label to a negative class instance.
  • It can typically be counted in a confusion matrix as part of classification performance evaluation.
  • It can typically contribute to the calculation of precision metric, false positive rate, and specificity measure.
  • It can typically correspond to a Type I Hypothesis Testing Error in statistical hypothesis testing contexts.
  • It can often be more costly than False Negative Classification in high-precision applications.
  • It can often trade off with False Negative Classification rates through classification threshold adjustment.
  • It can often result from class imbalance, overfitting, or feature noise.
  • It can often be reduced through threshold optimization, feature engineering, or ensemble methods.
  • It can often impact user trust and system credibility in production systems.
  • It can often require human review in critical decision systems.
  • It can range from being a Low-Cost False Positive to being a High-Cost False Positive, depending on its misclassification cost.
  • It can range from being a Random False Positive to being a Systematic False Positive, depending on its error pattern.
  • It can range from being a Borderline False Positive to being a Clear False Positive, depending on its prediction confidence.
  • It can range from being a Recoverable False Positive to being an Irrecoverable False Positive, depending on its consequence reversibility.
  • It can range from being a Single-Model False Positive to being an Ensemble False Positive, depending on its model agreement.
  • It can be a member of a False Positive Classification Set for calculating false positive rate.
  • It can be weighted differently in cost-sensitive learning and imbalanced classification.
  • It can be analyzed through error analysis, failure mode analysis, and misclassification patterns.
  • It can be visualized in ROC curves, precision-recall curves, and confusion matrix heatmaps.
  • ...
• Example(s):
  • Medical Diagnosis False Positives, such as:
    • Cancer Diagnosis False Positive where healthy patient diagnosed with cancer.
    • Disease Screening False Positive triggering unnecessary treatment.
    • Pregnancy Test False Positive showing positive for non-pregnant person.
    • Allergy Test False Positive indicating allergy that doesn't exist.
    • COVID-19 Test False Positive requiring unnecessary isolation.
  • Security System False Positives, such as:
    • Spam Filter False Positive blocking legitimate email message.
    • Fraud Detection False Positive flagging legitimate transaction.
    • Intrusion Detection False Positive alerting on normal network activity.
    • Face Recognition False Positive incorrectly matching different person.
    • Weapon Detection False Positive identifying harmless object as weapon.
  • Quality Control False Positives, such as:
    • Defect Detection False Positive rejecting good product.
    • Anomaly Detection False Positive flagging normal operation.
    • Software Bug False Positive reporting non-existent error.
    • Code Vulnerability False Positive identifying secure code as vulnerable.
  • Information Retrieval False Positives, such as:
    • Search Result False Positive returning irrelevant document.
    • Entity Recognition False Positive incorrectly identifying entity.
    • Duplicate Detection False Positive marking unique items as duplicates.
    • Plagiarism Detection False Positive flagging original work.
  • Predictive Model False Positives, such as:
    • Churn Prediction False Positive predicting customer will leave when they won't.
    • Default Prediction False Positive denying credit to creditworthy applicant.
    • Weather Prediction False Positive forecasting rain that doesn't occur.
    • Stock Signal False Positive triggering buy/sell on false pattern.
  • Legal System False Positives, such as:
    • Criminal Identification False Positive wrongly identifying suspect.
    • Contract Violation False Positive finding breach where none exists.
    • Evidence Match False Positive incorrectly linking evidence.
  • Hypothesis Testing Context:
    • Type I Error in statistical hypothesis testing (rejecting true null).
  • ...
• Counter-Example(s):
  • True Positive Classification, which correctly identifies positive instance.
  • True Negative Classification, which correctly identifies negative instance.
  • False Negative Classification, which incorrectly classifies positive as negative.
  • Type II Hypothesis Testing Error, which fails to detect true positive.
• See: Type I Hypothesis Testing Error (equivalent in hypothesis testing), Binary Classification Performance Measure, Confusion Matrix, Precision Metric, False Positive Rate, Specificity Measure, ROC Curve, Classification Threshold, Cost-Sensitive Learning.

---

---

References

2020

• (Wikipedia, 2020) ⇒ https://en.wikipedia.org/wiki/False_positives_and_false_negatives Retrieved:2020-10-5.
  • A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition such as a disease when the disease is not present, while a false negative is the opposite error where the test result incorrectly fails to indicate the presence of a condition when it is present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result, a Template:Visible anchor and a Template:Visible anchor.) They are also known in medicine as a false positive (respectively negative) diagnosis, and in statistical classification as a false positive (respectively negative) error.

    In statistical hypothesis testing the analogous concepts are known as type I and type II errors, where a positive result corresponds to rejecting the null hypothesis, and a negative result corresponds to not rejecting the null hypothesis. The terms are often used interchangeably, but there are differences in detail and interpretation due to the differences between medical testing and statistical hypothesis testing.

2006

• (Fawcett, 2006) ⇒ Tom Fawcett. (2006). “An Introduction to ROC Analysis.” In: Pattern Recognition Letters, 27(8). doi:10.1016/j.patrec.2005.10.010
  • QUOTE: Given a classifier and an instance, there are four possible outcomes. If the instance is positive and it is classified as positive, it is counted as a true positive; if it is classified as negative, it is counted as a false negative. If the instance is negative and it is classified as negative, it is counted as a true negative; if it is classified as positive, it is counted as a false positive. Given a classifier and a set of instances (the test set), a two-by-two confusion matrix (also called a contingency table) can be constructed representing the dispositions of the set of instances.

---