gelnet (Generalized ElasticNet) 사용법
Functions
gelnet
gelnet(X, y, l1, l2, nFeats=NULL, a=rep(1, n), d=rep(1, p),
P=diag(p), m=rep(0, p), max.iter=100, eps=1e-05,
w.init=rep(0, p), b.init=NULL, fix.bias=FALSE, silent=FALSE,
balanced=FALSE, nonneg=FALSE
)
R
복사
•
X: n-by-p matrix of n samples in p dimensions
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y: n-by-1 vector of response values. Must be numeric vector for regression, factor with 2 levels for binary classification, or NULL for a one-class task.
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l1: coefficient for the L1-norm penalty
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l2: coefficient for the L2-norm penalty
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nFeats: alternative parameterization that returns the desired number of non-zero weights. Takes precedence over l1 if not NULL
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a: n-by-1 vector of sample weights (regression only)
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d: p-by-1 vector of feature weights
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P: p-by-p feature association penalty matrix
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m: p-by-1 vector of translation coefficients
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max.iter: maximum number of iterations
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eps: convergence precision
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w.init: initial parameter estimate for the weights
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b.init: initial parameter estimate for the bias term
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fix.bias: set to TRUE to prevent the bias term from being updated (Regression only) (default: FALSE)
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silent: set to TRUE to suppress run-time output to stdout (default: FALSE)
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balanced: boolean specifying whether the balanced model is being trained (binary classification only) (default: FALSE)
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nonneg: set to TRUE to enforce non-negativity constraints on the weights (default: FALSE)