💡 Words with a Similar Meaning to "Linear predictor function"
Found via reverse dictionary — words that share a conceptual meaning.
| Word | Definition |
|---|---|
| linear regressionnoun | (statistics) A linear equation for deriving a single predicted value from one or more known explanatory values. |
| log-linear model | A log-linear model is a mathematical model that takes the form of a function whose logarithm equals a linear combination of the parameters of the model, which makes it possible to apply (possibly multivariate) linear regression. |
| regression analysis | In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the outcome or response variable, or a label in machine learning parlance) and one or more error-free independent variables (often called regressors, predictors, covariates, explanatory variables or features). |
| mean and predicted response | — |
| nonlinear regression | In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables. |
| generalized additive model | In statistics, a generalized additive model is a generalized linear model in which the linear response variable depends linearly on unknown smooth functions of some predictor variables, and interest focuses on inference about these smooth functions. |
| bayesian linear regression | a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables, with the goal of obtaining the posterior probability of the regression coefficients (as well as other parameters describing the distribution of the regressand) and ultimately allowing the out-of-sample prediction of the regressand (often labelled ) conditional on observed values of the regressors (usually ). |
| 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. |
| 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. |
| linear differential equation | In mathematics, a linear differential equation is a differential equation that is defined by a linear polynomial in the unknown function and its derivatives, that is an equation of the form |
| linear discriminant analysis | Linear discriminant analysis, normal discriminant analysis, or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics and other fields, to find a linear combination of features that characterizes or separates two or more classes of objects or events. |
| polynomial regression | In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modeled as an nth degree polynomial in x. Polynomial regression fits a nonlinear relationship between the value of x and the corresponding conditional mean of y, denoted E(y |x). |
| multicollinearitynoun | (statistics) A phenomenon in which two or more predictor variables in a multiple regression model are highly correlated, so that the coefficient estimates may change erratically in response to small changes in the model or data. |
| linear combinationnoun | (linear algebra) a sum, each of whose summands is an appropriate vector times an appropriate scalar (or ring element) |
| linear prediction | a mathematical operation where future values of a discrete-time signal are estimated as a linear function of previous samples. |
| linear interpolation | In mathematics, linear interpolation is a method of curve fitting using linear polynomials to construct new data points within the range of a discrete set of known data points. |
| probabilistic classification | In machine learning, a probabilistic classifier is a classifier that is able to predict, given an observation of an input, a probability distribution over a set of classes, rather than only outputting the most likely class that the observation should belong to. |
| generalized least squares | In statistics, generalized least squares is a method used to estimate the unknown parameters in a linear regression model. |
| likelihood function | A likelihood function (often simply called the likelihood) measures how well a statistical model explains observed data by calculating the probability of seeing that data under different parameter values of the model. |
| design matrix | In statistics and in particular in regression analysis, a design matrix, also known as model matrix or regressor matrix and often denoted by X, is a matrix of values of explanatory variables of a set of objects. |
Translate “Linear predictor function” into Another Language
Pick a language — the word will be pre-filled in the translator.