October 2017
Beginner to intermediate
572 pages
26h 1m
English
book
Machine Learning with R Cookbook - Second Edition
by AshishSingh Bhatia, Yu-Wei, Chiu (David Chiu)
Content preview from Machine Learning with R Cookbook - Second Edition
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How to do it...
Perform the following steps to calculate the error evolution of each ensemble learner:
- First, use the errorevol function to calculate the error evolution of the boosting classifiers:
> boosting.evol.train = errorevol(churn.boost, trainset)
> boosting.evol.test = errorevol(churn.boost, testset)
> plot(boosting.evol.test$error, type = "l", ylim = c(0, 1),
+ main = "Boosting error versus number of trees", xlab = "Iterations",
+ ylab = "Error", col = "red", lwd = 2)
> lines(boosting.evol.train$error, cex = .5, col = "blue", lty = 2, lwd = 2)
> legend("topright", c("test", "train"), col = c("red", "blue"), lty = 1:2, lwd = 2)
Boosting error versus number of trees
- Next, use the errorevol function to calculate the error evolution ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.