Binary classification in the context of Go/no-go

⭐ Core Definition: Binary classification

Binary classification is the task of putting things into one of two categories (each called a class). As such, it is the simplest form of the general task of classification into any number of classes. Typical binary classification problems include:

Medical testing to determine if a patient has a certain disease or not;
Quality control in industry, deciding whether a specification has been met;
In information retrieval, deciding whether a page should be in the result set of a search or not
In administration, deciding whether someone should be issued with a driving licence or not
In cognition, deciding whether an object is food or not food.

When measuring the accuracy of a binary classifier, the simplest way is to count the errors. But in the real world often one of the two classes is more important, so that the number of both of the different types of errors is of interest. For example, in medical testing, detecting a disease when it is not present (a false positive) is considered differently from not detecting a disease when it is present (a false negative).

↓ Menu

HINT:

👉 Binary classification in the context of Go/no-go

A go/no-go test is a two-step verification process that uses two boundary conditions, or a binary classification. The test is passed only when the go condition has been met and also the no-go condition has failed.

The test gives no information as to the degree of conformance to, or deviation from the boundary conditions. These tests can be used for statistical process control purposes. There are specific SPC tools that use parameter based measurements (e.g., P-charts) for determining the stability of a process.

↓ Explore More Topics

In this Dossier

⭐ Core Definition: Binary classification
👉 Binary classification in the context of Go/no-go
Binary classification in the context of Discretization
Binary classification in the context of False positive
Binary classification in the context of Multi-class classification
Binary classification in the context of False positives and false negatives

Binary classification in the context of Discretization

In applied mathematics, discretization is the process of transferring continuous functions, models, variables, and equations into discrete counterparts. This process is usually carried out as a first step toward making them suitable for numerical evaluation and implementation on digital computers. Dichotomization is the special case of discretization in which the number of discrete classes is 2, which can approximate a continuous variable as a binary variable (creating a dichotomy for modeling purposes, as in binary classification).

Discretization is also related to discrete mathematics, and is an important component of granular computing. In this context, discretization may also refer to modification of variable or category granularity, as when multiple discrete variables are aggregated or multiple discrete categories fused.

View the full Wikipedia page for Discretization

↑ Return to Menu

Binary classification in the context of False positive

Diagram of a binary classifier separating a set of samples into positive and negative values. The elements in the green area on the right are those classified as positive matches for the tested condition, while those on the pink area on the left were classified as negative matches.

The red crosses () within the green area () represent false positives (negative samples that were classified as positive).

Conversely, the green circles () within the pink area () represent false negatives (positive samples that were classified as negative).

View the full Wikipedia page for False positive

↑ Return to Menu

Binary classification in the context of Multi-class classification

In machine learning and statistical classification, multiclass classification or multinomial classification is the problem of classifying instances into one of three or more classes (classifying instances into one of two classes is called binary classification). For example, deciding on whether an image is showing a banana, peach, orange, or an apple is a multiclass classification problem, with four possible classes (banana, peach, orange, apple), while deciding on whether an image contains an apple or not is a binary classification problem (with the two possible classes being: apple, no apple).

While many classification algorithms (notably multinomial logistic regression) naturally permit the use of more than two classes, some are by nature binary algorithms; these can, however, be turned into multinomial classifiers by a variety of strategies.

View the full Wikipedia page for Multi-class classification

↑ Return to Menu

Binary classification in the context of False positives and false negatives

The red crosses () within the green area () represent false positives (negative samples that were classified as positive).

Conversely, the green circle () within the pink area () represents false negatives (positive samples that were classified as negative).

View the full Wikipedia page for False positives and false negatives

↑ Return to Menu

Binary classification in the context of Go/no-go

Binary classification Study page number 1 of 1

Play TriviaQuestions Online!

Skip to study material about Binary classification in the context of "Go/no-go"

⭐ Core Definition: Binary classification

👉 Binary classification in the context of Go/no-go

Binary classification in the context of Discretization

Binary classification in the context of False positive

Binary classification in the context of Multi-class classification

Binary classification in the context of False positives and false negatives