November 2017
Beginner
286 pages
8h 13m
English
book
Statistics for Data Science
by James C. Mott, Rajprasath Subramanian, Shaikh Salamatullah, James D. Miller, Vijayakumar Ramdoss
Content preview from Statistics for Data Science
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Prepping the data
First, various steps need to be performed on our example data.
The data is first loaded into an R data frame object named magic, recoding the CLASS output variable to use classes 1 and -1 for gamma rays and background radiation respectively:
> magic <- read.csv("magic04.data", header = FALSE)
> names(magic) <- c("FLENGTH", "FWIDTH", "FSIZE", "FCONC", "FCONC1",
"FASYM", "FM3LONG", "FM3TRANS", "FALPHA", "FDIST", "CLASS")
> magic$CLASS <- as.factor(ifelse(magic$CLASS =='g', 1, -1))
Next, the data is split into two files: a training data and a test data frame using an 80-20 split:
> library(caret) > set.seed(33711209) > magic_sampling_vector <- createDataPartition(magic$CLASS, p = 0.80, list = FALSE) > magic_train <- magic[magic_sampling_vector, ...
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