Table of Contents
39 relations: Annals of Statistics, Bayes estimator, Bayes' theorem, Bias of an estimator, Bias–variance tradeoff, Binary classification, Binary regression, Boosting (machine learning), Brier score, C4.5 algorithm, Calibration (statistics), CiteSeerX, Conditional probability, Cross-entropy, Data mining, Decision tree learning, Discrete choice, Document classification, Econometrics, Empirical risk minimization, Ensemble learning, Function (mathematics), Generative model, Isotonic regression, Logistic regression, Loss function, Machine learning, Multiclass classification, Multilayer perceptron, Naive Bayes classifier, Philip Dawid, Platt scaling, Prior probability, Probability distribution, Scoring rule, Set (mathematics), Statistical classification, Statistics, Support vector machine.
- Probabilistic models
Annals of Statistics
The Annals of Statistics is a peer-reviewed statistics journal published by the Institute of Mathematical Statistics.
See Probabilistic classification and Annals of Statistics
Bayes estimator
In estimation theory and decision theory, a Bayes estimator or a Bayes action is an estimator or decision rule that minimizes the posterior expected value of a loss function (i.e., the posterior expected loss).
See Probabilistic classification and Bayes estimator
Bayes' theorem
Bayes' theorem (alternatively Bayes' law or Bayes' rule, after Thomas Bayes) gives a mathematical rule for inverting conditional probabilities, allowing us to find the probability of a cause given its effect.
See Probabilistic classification and Bayes' theorem
Bias of an estimator
In statistics, the bias of an estimator (or bias function) is the difference between this estimator's expected value and the true value of the parameter being estimated.
See Probabilistic classification and Bias of an estimator
Bias–variance tradeoff
In statistics and machine learning, the bias–variance tradeoff describes the relationship between a model's complexity, the accuracy of its predictions, and how well it can make predictions on previously unseen data that were not used to train the model. Probabilistic classification and bias–variance tradeoff are statistical classification.
See Probabilistic classification and Bias–variance tradeoff
Binary classification
Binary classification is the task of classifying the elements of a set into one of two groups (each called class). Probabilistic classification and Binary classification are statistical classification.
See Probabilistic classification and Binary classification
Binary regression
In statistics, specifically regression analysis, a binary regression estimates a relationship between one or more explanatory variables and a single output binary variable.
See Probabilistic classification and Binary regression
Boosting (machine learning)
In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, variance.
See Probabilistic classification and Boosting (machine learning)
Brier score
The Brier Score is a ''strictly proper score function'' or ''strictly proper scoring rule'' that measures the accuracy of probabilistic predictions.
See Probabilistic classification and Brier score
C4.5 algorithm
C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan.
See Probabilistic classification and C4.5 algorithm
Calibration (statistics)
There are two main uses of the term calibration in statistics that denote special types of statistical inference problems. Probabilistic classification and calibration (statistics) are statistical classification.
See Probabilistic classification and Calibration (statistics)
CiteSeerX
X or CiteSeerX but DISPLAYTITLE only allows changing an initial letter to lower case --> CiteSeerX (formerly called CiteSeer) is a public search engine and digital library for scientific and academic papers, primarily in the fields of computer and information science.
See Probabilistic classification and CiteSeerX
Conditional probability
In probability theory, conditional probability is a measure of the probability of an event occurring, given that another event (by assumption, presumption, assertion or evidence) is already known to have occurred.
See Probabilistic classification and Conditional probability
Cross-entropy
In information theory, the cross-entropy between two probability distributions p and q, over the same underlying set of events, measures the average number of bits needed to identify an event drawn from the set when the coding scheme used for the set is optimized for an estimated probability distribution q, rather than the true distribution p.
See Probabilistic classification and Cross-entropy
Data mining
Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
See Probabilistic classification and Data mining
Decision tree learning
Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning.
See Probabilistic classification and Decision tree learning
Discrete choice
In economics, discrete choice models, or qualitative choice models, describe, explain, and predict choices between two or more discrete alternatives, such as entering or not entering the labor market, or choosing between modes of transport.
See Probabilistic classification and Discrete choice
Document classification
Document classification or document categorization is a problem in library science, information science and computer science.
See Probabilistic classification and Document classification
Econometrics
Econometrics is an application of statistical methods to economic data in order to give empirical content to economic relationships.
See Probabilistic classification and Econometrics
Empirical risk minimization
Empirical risk minimization is a principle in statistical learning theory which defines a family of learning algorithms based on evaluating performance over a known and fixed dataset.
See Probabilistic classification and Empirical risk minimization
Ensemble learning
In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.
See Probabilistic classification and Ensemble learning
Function (mathematics)
In mathematics, a function from a set to a set assigns to each element of exactly one element of.
See Probabilistic classification and Function (mathematics)
Generative model
In statistical classification, two main approaches are called the generative approach and the discriminative approach. Probabilistic classification and generative model are probabilistic models.
See Probabilistic classification and Generative model
Isotonic regression
In statistics and numerical analysis, isotonic regression or monotonic regression is the technique of fitting a free-form line to a sequence of observations such that the fitted line is non-decreasing (or non-increasing) everywhere, and lies as close to the observations as possible.
See Probabilistic classification and Isotonic regression
Logistic regression
In statistics, the logistic model (or logit model) is a statistical model that models the log-odds of an event as a linear combination of one or more independent variables. Probabilistic classification and logistic regression are statistical classification.
See Probabilistic classification and Logistic regression
Loss function
In mathematical optimization and decision theory, a loss function or cost function (sometimes also called an error function) is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event.
See Probabilistic classification and Loss function
Machine learning
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data and thus perform tasks without explicit instructions.
See Probabilistic classification and Machine learning
Multiclass 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). Probabilistic classification and multiclass classification are statistical classification.
See Probabilistic classification and Multiclass classification
Multilayer perceptron
A multilayer perceptron (MLP) is a name for a modern feedforward artificial neural network, consisting of fully connected neurons with a nonlinear activation function, organized in at least three layers, notable for being able to distinguish data that is not linearly separable.
See Probabilistic classification and Multilayer perceptron
Naive Bayes classifier
In statistics, naive Bayes classifiers are a family of linear "probabilistic classifiers" which assumes that the features are conditionally independent, given the target class. Probabilistic classification and naive Bayes classifier are statistical classification.
See Probabilistic classification and Naive Bayes classifier
Philip Dawid
Alexander Philip Dawid One or more of the preceding sentences incorporates text from the royalsociety.org website where: (pronounced 'David'; born 1 February 1946) is Emeritus Professor of Statistics of the University of Cambridge, and a Fellow of Darwin College, Cambridge.
See Probabilistic classification and Philip Dawid
Platt scaling
In machine learning, Platt scaling or Platt calibration is a way of transforming the outputs of a classification model into a probability distribution over classes. Probabilistic classification and Platt scaling are probabilistic models and statistical classification.
See Probabilistic classification and Platt scaling
Prior probability
A prior probability distribution of an uncertain quantity, often simply called the prior, is its assumed probability distribution before some evidence is taken into account.
See Probabilistic classification and Prior probability
Probability distribution
In probability theory and statistics, a probability distribution is the mathematical function that gives the probabilities of occurrence of possible outcomes for an experiment.
See Probabilistic classification and Probability distribution
Scoring rule
In decision theory, a scoring rule provides evaluation metrics for probabilistic predictions or forecasts.
See Probabilistic classification and Scoring rule
Set (mathematics)
In mathematics, a set is a collection of different things; these things are called elements or members of the set and are typically mathematical objects of any kind: numbers, symbols, points in space, lines, other geometrical shapes, variables, or even other sets.
See Probabilistic classification and Set (mathematics)
Statistical classification
When classification is performed by a computer, statistical methods are normally used to develop the algorithm.
See Probabilistic classification and Statistical classification
Statistics
Statistics (from German: Statistik, "description of a state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data.
See Probabilistic classification and Statistics
Support vector machine
In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms that analyze data for classification and regression analysis. Probabilistic classification and support vector machine are statistical classification.
See Probabilistic classification and Support vector machine
See also
Probabilistic models
- Bayesian approaches to brain function
- Binary Independence Model
- Constellation model
- Continuum structure function
- Convolutional deep belief network
- Deep belief network
- Divergence-from-randomness model
- Factored language model
- Fast probability integration
- First-order reliability method
- First-order second-moment method
- Flow-based generative model
- Generative model
- Graphical models
- Gutenberg–Richter law
- Infer.NET
- Language modeling
- Latent Dirichlet allocation
- ML.NET
- Maier's theorem
- Mixture model
- N-gram
- Pólya urn model
- Platt scaling
- Probabilistic automaton
- Probabilistic classification
- Probabilistic context-free grammar
- Probabilistic logic programming
- Probabilistic programming
- Probabilistic relevance model
- Probabilistic voting model
- Stochastic geometry models of wireless networks
- Stochastic grammar
- Stochastic models
References
Also known as Calibration plot, Class membership probabilities, Class-membership probabilities, Group membership probabilities, Group-membership probabilities, Probabilistic classifier.