Binary Classification Function
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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.