Binary Classification Function

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

• AKA: Binary Classifier, Two-Class Classifier, Predictive Decision Model, Two-valued Classifier, Binary Classification Model.
• Context:
  • It must have input of : Data Item(e.g. a Test Instance)
  • Function Output: Data Value from Binary Set.
  • It can be a Predictive Logic Relation if the Target Class is the Boolean Set.
  • 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 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).
• 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.

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References

2004

• (Hastie & 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.

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