Il s'agit de prÃ©dire la valeur d'une variables qualitative, i.e., de mettre les individus dans des classes. (Par exemple : aide au diagnostic mÃ©dical, reconnaissance des mauvais payeurs par une banque, etc.) On cherche des "fonctions linÃ©aires discirminantes (des combinaisons linÃ©aires dea variables, qui maximisent la variance interclasse et minimisent la variance intraclasse)

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*knnTree Construct or predict with k-nearest-neighbor classifiers, using cross-validation to select k, choose variables (by forward or backwards selection), and choose scaling (from among no scaling, scaling each column by its SD, or scaling each column by its MAD). The finished classifier will consist of a classification tree with one such k-nn classifier in each leaf.*

# Qui est le chef de la direction de PCH

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**There is a little-known phenomenon for binomial GLMs that was pointed out by Hauck & Donner (1977: JASA 72:851-3). The standard errors and t values derive from the Wald approximation to the log-likelihood, obtained by expanding the log-likelihood in a second-order Taylor expansion at the maximum likelihood estimates. If there are some \hat\beta_i which are large, the curvature of the log-likelihood at \hat{\vec{\beta}} can be much less than near \beta_i = 0, and so the Wald approximation underestimates the change in log-likelihood on setting \beta_i = 0. This happens in such a way that as |\hat\beta_i| \to \infty, the t statistic tends to zero. Thus highly significant coefficients according to the likelihood ratio test may have non-significant t ratios.**

# citations tirages au sort

knnTree Construct or predict with k-nearest-neighbor classifiers, using cross-validation to select k, choose variables (by forward or backwards selection), and choose scaling (from among no scaling, scaling each column by its SD, or scaling each column by its MAD). The finished classifier will consist of a classification tree with one such k-nn classifier in each leaf.## Depuis combien de temps est paiement de loto