# Binary Classification Function

A Binary Classification Function is a classification function whose class set is a binary set.

**AKA:**Two-Class Classifier, 2-Valued Classification Model.**Context:****input**: Data Item(e.g. a Test Instance)**range:**Data Value from Binary Set.- It can range from being an Exact Binary Classification Function to being a Predictive Binary Classification Function.
- It can make a Correct Prediction or an Incorrect Prediction.
- Its Performance can be measured in terms of:
- True Positive Rate (Recall/Sensitivity), True Negative Rate, Precision, Fallout, Accuracy, F-Score.

- It can be an Input to a Binary Classification Task.

**Example:**- a Boolean Logic Function.
- A Credit Risk Model that predicts whether a person is a good credit risk (Yes, No).
- A Predictive Logic Relation (based on a Logic Relation).
- A Logistic Classification Function (based on a Logistic Function).
- a Decision Tree Classification Function (based on a Tree Data Structure).
- a Customer Defection Model.

**Counter-Example(s):**- a Multiclass Classification Function.
- a Continuous Function.
- a Weather Forecasting Model that predicts whether there will be frost tomorrow, the day after, and the subsequent day.

**See:**Relation Function, Binomial Regression Model.

## References

### 2004

- (Hastie et al., 2004) ⇒ Trevor Hastie, Saharon Rosset, Robert Tibshirani, and Ji Zhu. (2004). “The Entire Regularization Path for the Support Vector Machine.” In: The Journal of Machine Learning Research, 5.
- QUOTE:The support vector machine (SVM) is a widely used tool for classification. Many efficient implementations exist for fitting a two-class SVM model. The user has to supply values for the tuning parameters: the regularization cost parameter, and the kernel parameters. In this paper we study the support vector machine (SVM)(Vapnik, 1996; Schölkopf and Smola, 2001) for two-class classification.