In statistics and in machine learning, a linear predictor function is a linear function (linear combination) of a set of coefficients and explanatory variables (independent variables), whose value is used to predict the outcome of a dependent variable.[1] This sort of function usually comes in linear regression, where the coefficients are called regression coefficients. However, they also occur in various types of linear classifiers (e.g. logistic regression,[2] perceptrons,[3] support vector machines,[4] and linear discriminant analysis[5]), as well as in various other models, such as principal component analysis[6] and factor analysis. In many of these models, the coefficients are referred to as "weights".
A simple regression equation has on the right hand side an intercept and an explanatory variable with a slope coefficient. A multiple regression equation has two or more explanatory variables on the right hand side, each with its own slope coefficient
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