X <- as.data.frame(matrix(rnorm(1000), ncol=10))
y <- factor(ifelse(apply(X, 1, mean) > 0, 1, 0))
learn <- cbind(y, X)
mt <- bagging(y ~., data=learn, coob=TRUE)
mt
X <- as.data.frame(matrix(rnorm(1000), ncol=10))
y <- factor(ifelse(apply(X, 1, mean) > 0, 1, 0))
cls <- predict(mt, newdata=X)
cat("Misclass error est: ", mean(y != cls), "")
cat("Misclass error oob: ", mt$err, "")
X <- as.data.frame(matrix(rnorm(1000), ncol=10))
y <- apply(X, 1, mean) + rnorm(nrow(X))
learn <- cbind(y, X)
mt <- bagging(y ~., data=learn, coob=TRUE)
mt
X <- as.data.frame(matrix(rnorm(1000), ncol=10))
y <- apply(X, 1, mean) + rnorm(nrow(X))
haty <- predict(mt, newdata=X)
cat("MSE error: ", mean((haty - y)^2) , "")
data(BreastCancer)
BreastCancer$Id <- NULL
# Test set error bagging (nbagg = 50): 3.7\% (Breiman, 1998, Table 5)
bagging(Class ~ Cl.thickness + Cell.size
+ Cell.shape + Marg.adhesion
+ Epith.c.size + Bare.nuclei
+ Bl.cromatin + Normal.nucleoli
+ Mitoses, data=BreastCancer, coob=TRUE)
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