Description Usage Arguments Value Functions References See Also Examples
Loss functions to perform a regression
1 2 3 4 5 6 7 | lmsRegressionLoss(x, y, loss.weights = 1)
ladRegressionLoss(x, y, loss.weights = 1)
quantileRegressionLoss(x, y, q = 0.5, loss.weights = 1)
epsilonInsensitiveRegressionLoss(x, y, epsilon, loss.weights = 1)
|
x |
matrix of training instances (one instance by row) |
y |
numeric vector of values representing the training labels for each instance in x |
loss.weights |
numeric vector of loss weights to incure for each instance of x. Vector length should match length(y), but values are cycled if not of identical size. |
q |
a numeric value in the range [0-1] defining quantile value to consider |
epsilon |
a numeric value setting tolerance of the epsilon-regression |
a function taking one argument w and computing the loss value and the gradient at point w
lmsRegressionLoss
: Least Mean Square regression
ladRegressionLoss
: Least Absolute Deviation regression
quantileRegressionLoss
: Quantile Regression
epsilonInsensitiveRegressionLoss
: epsilon-insensitive regression (Vapnik et al. 1997)
Teo et al. Bundle Methods for Regularized Risk Minimization JMLR 2010
nrbm
1 2 3 4 5 6 | x <- cbind(intercept=100,data.matrix(iris[1:2]))
y <- iris[[3]]
w <- nrbm(lmsRegressionLoss(x,y))
w <- nrbm(ladRegressionLoss(x,y))
w <- nrbm(quantileRegressionLoss(x,y,q=0.5))
w <- nrbm(epsilonInsensitiveRegressionLoss(x,y,epsilon=1))
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