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Dividing things between two categories
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
Binary_classification
Putting things into categories
Different fields have taken different approaches, even in binary classification (see Evaluation of binary classifiers). In pattern recognition, error rate is
Classification
Measures of observational error
data. Accuracy is also used as a statistical measure of how well a binary classification test correctly identifies or excludes a condition. That is, the
Accuracy_and_precision
Categorization of data using statistics
observation. Classification can be thought of as two separate problems – binary classification and multiclass classification. In binary classification, a better
Statistical_classification
Problem in machine learning and statistical classification
called binary classification). For example, deciding on whether an image is showing a banana, peach, orange, or an apple is a multiclass classification problem
Multiclass_classification
Statistical measure of a test's accuracy
In statistical analysis of binary classification and information retrieval systems, the F-score or F-measure is a measure of predictive performance. It
F-score
Measure of similarity and diversity between sets
metric space under this function. In confusion matrices employed for binary classification, the Jaccard index can be framed in the following formula: Jaccard
Jaccard_index
Classification problem where multiple labels may be assigned to each instance
methods exist for multi-label classification, and can be roughly broken down into: The baseline approach, called the binary relevance method, amounts to
Multi-label_classification
Multiple countries legally recognize non-binary or third gender classifications. These classifications are typically based on a person's gender identity
Legal recognition of non-binary gender
Legal_recognition_of_non-binary_gender
Statistic measuring inter-rater agreement for categorical items
matrix employed in machine learning and statistics to evaluate binary classifications, the Cohen's Kappa formula can be written as: κ = 2 × ( T P × T
Cohen's_kappa
of the elements to be classified. A special kind of classification rule is binary classification, for problems in which there are only two classes. Given
Classification_rule
Wi-Fi technology
technology can be broadly categorized into four domains: Detection (binary classification, e.g. intruder detection, fall-down detection, presence detection)
WiFi_Sensing
Mathematical function conceived as a crude model
the task, these functions could have a sigmoid shape (e.g. for binary classification), but they may also take the form of other nonlinear functions,
Artificial_neuron
Diagnostic plot of binary classifier ability
problem (binary classification), in which the outcomes are labeled either as positive (p) or negative (n). There are four possible outcomes from a binary classifier
Receiver operating characteristic
Receiver_operating_characteristic
Binary measure of democracy and dictatorship
Jennifer Gandhi, and James Raymond Vreeland. Based on the regime binary classification idea proposed by Mike Alvarez in 1996, and the Democracy and Development
Democracy-Dictatorship_Index
Statistical regression method
showed that for every instance of the elastic net, an artificial binary classification problem can be constructed such that the hyper-plane solution of
Elastic_net_regularization
Pass/fail test principle using two conditions
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
Go/no-go
Two astronomical bodies which orbit each other
of binary system are binary stars and binary asteroids, but brown dwarfs, planets, neutron stars, black holes and galaxies can also form binaries. A multiple
Binary_system
Concept in machine learning
expected risk, see empirical risk minimization. In the case of binary classification, it is possible to simplify the calculation of expected risk from
Loss functions for classification
Loss_functions_for_classification
Interdisciplinary research area
the outcome of the measurement of a qubit reveals the result of a binary classification task. While many proposals of QML algorithms are still purely theoretical
Quantum_machine_learning
Table layout for visualizing performance; also called an error matrix
classification performance, it may give an incomplete picture of a model's true reliability. Confusion matrix is not limited to binary classification
Confusion_matrix
Statistical model for a binary dependent variable
regression is a supervised machine learning algorithm widely used for binary classification tasks, such as identifying whether an email is spam or not and diagnosing
Logistic_regression
Statistics about search result quality
\{{\mbox{retrieved documents}}\}|}{|\{{\mbox{retrieved documents}}\}|}}} In binary classification, precision is analogous to positive predictive value. Precision
Evaluation measures (information retrieval)
Evaluation_measures_(information_retrieval)
Statistical estimation method
prediction (binary classification), or for estimating the association between the explanatory variables and the output. In economics, binary regressions
Binary_regression
Topics referred to by the same term
Sensitivity and specificity, statistical measures of the performance of binary classification tests antimicrobial susceptibility, often called "sensitivity" Allergic
Sensitivity
Type of feedforward neural network
function. For binary classification (predicting yes/no), it uses a single neuron with a sigmoid activation. For multi-class classification, it usually has
Multilayer_perceptron
Pattern-recognition performance metrics
definitions of precision, recall, and F-score are formulated for binary classification, where each instance is either true or false. Many practical domains
Precision_and_recall
Conversion of continuous functions into discrete counterparts
approximate a continuous variable as a binary variable (creating a dichotomy for modeling purposes, as in binary classification). Discretization is also related
Discretization
Error rate graph
error tradeoff (DET) graph is a graphical plot of error rates for binary classification systems, plotting the false rejection rate vs. false acceptance
Detection_error_tradeoff
Topological model
are 512 possible 2D topologic relations, that can be grouped into binary classification schemes. The English language contains about 10 schemes (relations)
DE-9IM
Statistical regression where the dependent variable can take only two values
predicted probabilities is a type of binary classification model. A probit model is a popular specification for a binary response model. As such it treats
Probit_model
Measure of the effectiveness of a diagnostic test
In medical testing with binary classification, the diagnostic odds ratio (DOR) is a measure of the effectiveness of a diagnostic test. It is defined as
Diagnostic_odds_ratio
Natural interconnection of food chains
classify organisms as autotrophs or heterotrophs. This is a non-binary classification; some organisms (such as carnivorous plants) occupy the role of
Food_web
Machine learning calibration technique
other classification models. Platt scaling works by fitting a logistic regression model to a classifier's scores. Consider the problem of binary classification:
Platt_scaling
Machine learning algorithm
generalizes k-nearest neighbors (k-NN). k-NN supports binary classification, multiclass classification, and regression, whereas SkNN allows training of a
Structured_kNN
Quantitative measurement of accuracy
Evaluation of a binary classifier typically assigns a numerical value, or values, to a classifier that represent its accuracy. An example is error rate
Evaluation of binary classifiers
Evaluation_of_binary_classifiers
Algorithm for supervised learning of binary classifiers
learning, the perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a function that can decide whether or not an input
Perceptron
Topics referred to by the same term
Look up binary in Wiktionary, the free dictionary. Binary may refer to: Binary number, a representation of numbers using only two values (0 and 1) for
Binary
result of a test, often in regard to medical tests. In cases where binary classification can be applied to the test results (such as yes versus no, test
Predictive_value_of_tests
Feature selection algorithm used in binary classification
feature interactions. It was originally designed for application to binary classification problems with discrete or numerical features. Relief calculates
Relief_(feature_selection)
Gender identities outside of the gender binary
Non-binary (also written as nonbinary) or genderqueer gender identities are those that are outside the male/female gender binary. Non-binary identities
Non-binary
Historical computer
I Perceptron as early as 1958, Rosenblatt demonstrated a simple binary classification experiment, namely distinguishing between sheets of paper marked
Mark_I_Perceptron
Measure of the accuracy of probabilistic predictions
present data set being scored. In this default case, for binary (two-class) classification, the reference Brier score is given by (using the notation
Brier_score
Type of statistical inference
example of learning which is not inductive would be in the case of binary classification, where the inputs tend to cluster in two groups. A large set of
Transduction (machine learning)
Transduction_(machine_learning)
learning is a logical approach to machine learning, specifically binary classification. Version space learning algorithms search a predefined space of
Version_space_learning
System of two stars orbiting each other
A binary star or binary star system is a system of two stars that are gravitationally bound to and in orbit around each other. Binary stars are among
Binary_star
Mathematical model used for classification or regression
for classification and regression, where the main goal is accurate prediction on new data. They are typically used to solve binary classification problems
Discriminative_model
Machine learning library
engineering like n-gram creation, and learners to handle binary classification, multi-class classification, and regression tasks. Additional ML tasks like anomaly
ML.NET
Types of error in data reporting
A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when
False positives and false negatives
False_positives_and_false_negatives
Machine learning paradigm
meaningful representation of the data in its latent space. For a binary classification task, training data can be divided into positive examples and negative
Self-supervised_learning
Human chromosomal condition
a great deal of energy in the attempt to include XXY within the binary classification "Intersex conditions". Intersex Society of North America. Retrieved
Klinefelter_syndrome
Set of methods for supervised statistical learning
Classifying data is a common task in machine learning. Say we have a binary classification problem: suppose some given data points each belong to one of two
Support_vector_machine
Topics referred to by the same term
(disambiguation) Specificity (statistics), the proportion of negatives in a binary classification test which are correctly identified Sensitivity and specificity
Specificity
Measure for evaluating probabilistic forecasts
target variables in mind. Scoring rules exist for binary and categorical probabilistic classification, as well as for univariate and multivariate probabilistic
Scoring_rule
Machine learning technique
can use Laplace distribution, or Student's t-distribution. For binary classification, it also proposed logistic regression experts, with f i ( y | x
Mixture_of_experts
Machine learning algorithm
(SVM) classifier. Whereas the SVM classifier supports binary classification, multiclass classification and regression, the structured SVM allows training
Structured support vector machine
Structured_support_vector_machine
Measured values that are relatively normal for a particular medical test
thus how to treat it. A cutoff or threshold is a limit used for binary classification, mainly between normal versus pathological (or probably pathological)
Reference_range
Bayesian diagnostic threshold derived from the geometry of screening curves
prevalence-threshold framework can be written in the language of binary classification. In that setting, disease prevalence corresponds to the base rate
Prevalence_threshold
Type of supervised learning in machine learning
containing many instances. In the simple case of multiple-instance binary classification, a bag may be labeled negative if all the instances in it are negative
Multiple_instance_learning
Statistical measures of whether a finding is likely to be true
of the predictive value termed the Etiologic Predictive Value. Binary classification Sensitivity and specificity False discovery rate Relevance (information
Positive and negative predictive values
Positive_and_negative_predictive_values
Adaptive boosting based classification algorithm
output of the boosted classifier. Usually, AdaBoost is presented for binary classification, although it can be generalized to multiple classes or bounded intervals
AdaBoost
Statistical measure of a binary classification
Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation". BMC Genomics. 21 (1): 6-1–6-13. doi:10.1186/s12864-019-6413-7
Sensitivity_and_specificity
Concepts from statistical hypothesis testing
characteristic – Diagnostic plot of binary classifier ability Sensitivity and specificity – Statistical measure of a binary classification Statisticians' and engineers'
Type_I_and_type_II_errors
Regression analysis technique
considered a special case of probabilistic classification, and thus a generalization of binary classification. In one published example of an application
Binomial_regression
Properties not expressed numerically
qualitative data about something. This can be a categorical result or a binary classification (e.g., pass/fail, go/no go, conform/non-conform). It can sometimes
Qualitative_property
social sciences, unit-weighted regression is sometimes used for binary classification, i.e. to predict a yes-no answer where y ^ < 0 {\displaystyle {\hat
Unit-weighted_regression
Optimization algorithm for artificial neural networks
For classification the last layer is usually the logistic function for binary classification, and softmax (softargmax) for multi-class classification, while
Backpropagation
Literature analysis technique
bimorphisms, as well as surgical transsexuals [...] defy attempts at binary classification". This thinking, along with the advent of a more prominent LGBT
Critical_lens
Differing classification systems of demons
at the classification of demons within the contexts of classical mythology, demonology, occultism, and Renaissance magic. These classifications may be
Classification_of_demons
estimation.[citation needed] Mutual information Rand index F-score Binary classification Claude E. Shannon; Warren Weaver (1963). The Mathematical Theory
Uncertainty_coefficient
Statistical model validation technique
value is approximately equal in all the partitions. In the case of binary classification, this means that each partition contains roughly the same proportions
Cross-validation_(statistics)
Statistical sampling techniques
are available in the smote-variants package. Poor models in [the binary classification] setting are often a result of—any combination of—fitting deterministic
Oversampling and undersampling in data analysis
Oversampling_and_undersampling_in_data_analysis
sometimes used in American literature to present an alternative to the binary classification which notes the preferred sexual position, such as top or bottom;
Terminology_of_homosexuality
Indian-American statistician (1924–1997)
Anderson which is used in statistics and engineering for solving binary classification problems when the underlying data have multivariate normal distributions
Raghu_Raj_Bahadur
International relations theory
the democracy scale and belligerence; others have treated it as a binary classification by (as its maker does) calling all states with a high democracy
Democratic_peace_theory
Series of language models developed by Google AI
final vector for the [CLS] token is passed to a linear layer for binary classification into [IsNext] and [NotNext]. For example: Given "[CLS] my dog is
BERT_(language_model)
Topics referred to by the same term
measures False positives and false negatives, types of errors in binary classification Positive or negative test, possible result of a medical test Negative
Positive and negative (disambiguation)
Positive_and_negative_(disambiguation)
Statistical measure of association for two binary variables
bioinformatics and machine learning to evaluate the quality of binary (two-class) classifications. It is named for biochemist Brian W. Matthews, who described
Phi_coefficient
Notion in supervised machine learning
{\displaystyle {\mathcal {C}}} is ∞ {\displaystyle \infty } . Consider a binary classification model f {\displaystyle f} with some parameter vector θ {\displaystyle
Vapnik–Chervonenkis_dimension
Austronesian language of Madagascar
north (excluding the far northern tip) are considered Eastern. This binary classification is now widely viewed as outdated. It overlooks crucial grammatical
Malagasy_language
Gun modification for faster firing
function of the trigger. This allows guns outfitted with a binary trigger to avoid classification as a machine gun within the definitions used by United States
Binary_trigger
Selection of data points in statistics
in Anderson minimax ratio whose value is proved to be 0.5: in a binary classification, the class-sample sizes should be chosen equally. This ratio can
Sampling_(statistics)
Statistical classification in machine learning
assumptions. It is in essence a method of dimensionality reduction for binary classification. Support vector machine—an algorithm that maximizes the margin between
Linear_classifier
Measure of similarity between two data clusterings
negatives. The Rand index can also be viewed through the prism of binary classification accuracy over the pairs of elements in S {\displaystyle S} . The
Rand_index
Classification of sex and gender into two opposite forms
The gender binary (also known as gender binarism) is the classification of gender into two distinct forms of masculine and feminine, whether by social
Gender_binary
is designed specifically for performing and assessing a standard binary classification test (disease vs. control). ROCCET accepts metabolite data tables
Receiver Operating Characteristic Curve Explorer and Tester
Receiver_Operating_Characteristic_Curve_Explorer_and_Tester
Measurable property or characteristic
conveniently described by a feature vector. One way to achieve binary classification is using a linear predictor function (related to the perceptron)
Feature_(machine_learning)
Feature of systems that defy description
is the most beneficial and could be expanded to other areas. For binary classification, such measures can consider the overlaps in feature values from
Complexity
Class of binary stars
X-ray binaries are a class of binary stars that are luminous in X-rays. The X-rays are produced by matter falling from one component, called the donor
X-ray_binary
Notion in computational learning theory
{\displaystyle S} . A general result, proved by Vladimir Vapnik for an ERM binary classification algorithms, is that for any target function and input distribution
Stability_(learning_theory)
Framework for machine learning
if the predicted output is different from the actual output. For binary classification with Y = { − 1 , 1 } {\displaystyle Y=\{-1,1\}} , this is: V ( f
Statistical_learning_theory
Measure of ordinal association
dependent variable Y is a binary variable, i.e. for binary classification or prediction of binary outcomes including binary choice models in econometrics
Somers'_D
Collapsed core of a massive star
stars that have been detected are pulsars or a part of a binary system. Neutron stars in a binary system with a main sequence star can pull in large amounts
Neutron_star
Free and open-source statistical program
Bayesian statistics with simple examples and supporting text (with Binary Classification, Counts, The Problem of Points, Buffon’s Needle) Learn Stats: Learn
JASP
Process of automating the application of machine learning
categorical text feature, or free text feature Task detection; e.g., binary classification, regression, clustering, or ranking Feature engineering Feature
Automated_machine_learning
Assuming the socially lower ancestry
the number of intervening generations. The same Act established a binary classification system for vital records, classifying people as 'white' or 'black'
Hypodescent
Property in knot theory
an isotopy invariant of tame knots. Because tricolorability is a binary classification (a link is either tricolorable or not*), it is a relatively weak
Tricolorability
Algorithm for solving the quadratic programming problem from training SVMs
Consider a binary classification problem with a dataset (x1, y1), ..., (xn, yn), where xi is an input vector and yi ∈ {-1, +1} is a binary label corresponding
Sequential minimal optimization
Sequential_minimal_optimization
Flaw of binary classification
Flaw of binary classification
Accuracy_paradox
Quantifying marketing influence
the primary method for measuring true marketing effectiveness. Binary classification methods from statistics and machine learning can be used to build
Attribution_(marketing)
travel, tourism, insurance
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travel, tourism, insurance