Êtes-vous un étudiant de l'EPFL à la recherche d'un projet de semestre?
Travaillez avec nous sur des projets en science des données et en visualisation, et déployez votre projet sous forme d'application sur Graph Search.
Concept
Linear predictor function
Résumé
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. 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, perceptrons, support vector machines, and linear discriminant analysis), as well as in various other models, such as principal component analysis and factor analysis. In many of these models, the coefficients are referred to as "weights".
The basic form of a linear predictor function for data point i (consisting of p explanatory variables), for i = 1, ..., n, is
where , for k = 1, ..., p, is the value of the k-th explanatory variable for data point i, and are the coefficients (regression coefficients, weights, etc.) indicating the relative effect of a particular explanatory variable on the outcome.
It is common to write the predictor function in a more compact form as follows:
The coefficients β0, β1, ..., βp are grouped into a single vector β of size p + 1.
For each data point i, an additional explanatory pseudo-variable xi0 is added, with a fixed value of 1, corresponding to the intercept coefficient β0.
The resulting explanatory variables xi0(= 1), xi1, ..., xip are then grouped into a single vector xi of size p + 1.
This makes it possible to write the linear predictor function as follows:
using the notation for a dot product between two vectors.
An equivalent form using matrix notation is as follows:
where and are assumed to be a (p+1)-by-1 column vectors, is the matrix transpose of (so is a 1-by-(p+1) row vector), and indicates matrix multiplication between the 1-by-(p+1) row vector and the (p+1)-by-1 column vector, producing a 1-by-1 matrix that is taken to be a scalar.
Source officielle
À propos de ce résultat
Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.
Proximité ontologique
Cours associés (9)
EE-612: Fundamentals in statistical pattern recognition
This course provides in-depth understanding of the most fundamental algorithms in statistical pattern recognition or machine learning (including Deep Learning) as well as concrete tools (as Python sou
MGT-448: Statistical inference and machine learning
This course aims to provide graduate students a thorough grounding in the methods, theory, mathematics and algorithms needed to do research and applications in machine learning. The course covers topi
MGT-502: Data science and machine learning
Hands-on introduction to data science and machine learning. We explore recommender systems, generative AI, chatbots, graphs, as well as regression, classification, clustering, dimensionality reduction