Binary Classification Decision
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A Binary Classification Decision is a classification decision that assigns an input item to exactly one of two mutually exclusive classes based on decision criteria.
- AKA: Two-Class Decision, Dichotomous Decision, Binary Decision, Either-Or Classification, Yes/No Decision.
- Context:
- It can typically require a Decision Threshold for class boundary determination.
- It can typically produce a Binary Outcome with confidence measure.
- It can typically utilize Decision Functions that map input space to binary choice.
- It can often employ Discriminant Analysis for class separation.
- It can often incorporate Prior Probabilitys through Bayesian reasoning.
- It can often generate Decision Confidence Scores for reliability assessment.
- It can range from being a Deterministic Binary Classification Decision to being a Probabilistic Binary Classification Decision, depending on its output nature.
- It can range from being a Balanced Binary Classification Decision to being an Imbalanced Binary Classification Decision, depending on its class distribution.
- It can range from being a Linear Binary Classification Decision to being a Non-Linear Binary Classification Decision, depending on its decision boundary.
- It can range from being a Cost-Symmetric Binary Classification Decision to being a Cost-Asymmetric Binary Classification Decision, depending on its error cost structure.
- It can range from being a Conservative Binary Classification Decision to being an Aggressive Binary Classification Decision, depending on its threshold setting.
- It can be evaluated through Binary Classification Metrics including accuracy, precision, recall.
- It can produce Type I Errors (false positives) and Type II Errors (false negatives).
- It can utilize ROC Analysis for threshold optimization.
- It can employ Decision Trees, Logistic Regression, or Support Vector Machines.
- It can support Sequential Binary Decisions through decision cascades.
- ...
- Example(s):
- Statistical Binary Classification Decisions, such as:
- Null Hypothesis Rejection Decision determining reject/fail-to-reject based on p-value.
- Outlier Detection Decision classifying points as normal/anomalous.
- Significance Test Decision concluding significant/non-significant results.
- Medical Binary Classification Decisions, such as:
- Disease Diagnosis Decision determining present/absent conditions.
- Treatment Eligibility Decision classifying patients as eligible/ineligible.
- Risk Stratification Decision categorizing patients as high-risk/low-risk.
- Business Binary Classification Decisions, such as:
- Credit Approval Decision determining approve/deny for loan applications.
- Fraud Detection Decision classifying transactions as legitimate/fraudulent.
- Customer Churn Decision predicting will-stay/will-leave customers.
- Quality Control Binary Classification Decisions, such as:
- Pass/Fail Decision for product inspection.
- Conformance Decision determining in-spec/out-of-spec items.
- Defect Classification Decision identifying defective/non-defective units.
- Security Binary Classification Decisions, such as:
- Access Control Decision granting allow/deny permissions.
- Threat Detection Decision classifying activity as safe/malicious.
- Authentication Decision determining legitimate/illegitimate users.
- Information Retrieval Binary Classification Decisions, such as:
- Relevance Decision classifying documents as relevant/irrelevant.
- Spam Classification Decision determining spam/legitimate messages.
- Duplicate Detection Decision identifying unique/duplicate records.
- ...
- Statistical Binary Classification Decisions, such as:
- Counter-Example(s):
- Multiclass Classification Decision, which selects among more than two classes.
- Regression Decision, which produces continuous values rather than binary choices.
- Ranking Decision, which orders items rather than binary classification.
- Clustering Decision, which groups without predefined binary classes.
- Probabilistic Assessment, which provides likelihood without binary decision.
- Graded Classification Decision, which uses ordinal categories rather than binary.
- See: Binary Classification Task, Decision Threshold, Confusion Matrix, ROC Curve, Binary Classifier, Type I Error, Type II Error, Sensitivity and Specificity, Positive Predictive Value, Negative Predictive Value.