Binary Class Prediction Task

A Binary Class Prediction Task is a class prediction task whose class set is a binary set (with a binary target).

• AKA: Binomial Pattern Recognition.
• Context:
  • Input: a Classification Record.
  • output: a Positive or Negative Label.
    • optional: a Confidence Score.
    • optional: a Binary Classification Structure.
  • measure: Boolean Classification Performance Measure.
  • measure(s): Precision, Recall and F-Measure.
  • It can be solved by a Binary Classification System (that implements a binary classification algorithm).
  • It can range from being a Heuristic Binary Classification to being a Data-Driven Binary Classification (such as supervised binary classification).
• Example(s):
  • a Spam Detection Task.
  • …
• Counter-Example(s):
  • Multiclass Classification Task.
• See: Statistical Hypothesis Testing, Boolean Variable.

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References

2018

• (Google ML Glossary, 2018) ⇒ (2008). binary Classification. In: Machine Learning Glossary https://developers.google.com/machine-learning/glossary/ Retrieved: 2018-05-13.
  • QUOTE: A type of classification task that outputs one of two mutually exclusive classes. For example, a machine learning model that evaluates email messages and outputs either “spam” or "not spam" is a binary classifier.

2014

• (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/binary_classification Retrieved:2014-11-10.
  • Binary or binomial classification is the task of classifying the elements of a given set into two groups on the basis of a classification rule. Some typical binary classification tasks are:
    • medical testing to determine if a patient has certain disease or not – the classification property is the presence of the disease;
    • a "pass or fail" test method or quality control in factories; i.e. deciding if a specification has or has not been met: a Go/no go classification.
    • information retrieval, namely deciding whether a page or an article should be in the result set of a search or not – the classification property is the relevance of the article, or the usefulness to the user.
  • An important point is that in many practical binary classification problems, the two groups are not symmetric – rather than overall accuracy, the relative proportion of different types of errors is of interest. For example, in medical testing, a false positive (detecting a disease when it is not present) is considered differently from a false negative (not detecting a disease when it is present).

     Statistical classification in general is one of the problems studied in computer science, in order to automatically learn classification systems; some methods suitable for learning binary classifiers include the decision trees, Bayesian networks, support vector machines, neural networks, probit regression, and logit regression.

    Sometimes, classification tasks are trivial. Given 100 balls, some of them red and some blue, a human with normal color vision can easily separate them into red ones and blue ones. However, some tasks, like those in practical medicine, and those interesting from the computer science point of view, are far from trivial, and may produce faulty results if executed imprecisely.

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:In this paper we study the support vector machine (SVM)(Vapnik, 1996; Schölkopf and Smola, 2001) for 'two-class classification. We have a set of [math]\displaystyle{ n }[/math] training pairs x_i, y_i, where x_i in R^p is a p-vector of real-valued predictors (attributes) for the i^th observation, and y_i in {−1,+1} codes its binary response. We start off with the simple case of a linear classifier, where our goal is to estimate a linear decision function :[math]\displaystyle{ f(x) = \beta_0+\beta^Tx }[/math], and its associated classifier [math]\displaystyle{ \text{Class}(x) = \text{sign}[ƒ(x)] }[/math].

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