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 perform dimension reduction using SVD:
- First, you can perform svd on the swiss dataset:
> swiss.svd = svd(swiss)
- You can then plot the percentage of variance explained and the cumulative variance explained in accordance with the SVD column:
> plot(swiss.svd$d^2/sum(swiss.svd$d^2), type="l", xlab=" Singular vector", ylab = "Variance explained")
The percent of variance explained
> plot(cumsum(swiss.svd$d^2/sum(swiss.svd$d^2)), type="l", xlab="Singular vector", ylab = "Cumulative percent of variance explained")
Cumulative percent of variance explained
- Next, you can reconstruct the data ...
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