E Solutions ch. 6 - Decision trees and random forests
Solutions to exercises of chapter 6.
E.1 Exercise 1
Load the necessary packages
readr to read in the data
dplyr to process data
party and rpart for the classification tree algorithms
library(readr)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(party)
## Loading required package: methods
## Loading required package: grid
## Loading required package: mvtnorm
## Loading required package: modeltools
## Loading required package: stats4
## Loading required package: strucchange
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
## Loading required package: sandwich
library(rpart)
library(rpart.plot)
library(ROCR)
## Loading required package: gplots
##
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
##
## lowess
set.seed(100)
Select features that may explain survival
Each row in the data is a passenger. Columns are features:
survived: 0 if died, 1 if survived
embarked: Port of Embarkation (Cherbourg, Queenstown,Southampton)
sex: Gender
sibsp: Number of Siblings/Spouses Aboard
parch: Number of Parents/Children Aboard
fare: Fare Payed
Make categorical features should be made into factors
titanic3 <- "https://goo.gl/At238b" %>%
read_csv %>% # read in the data
select(survived, embarked, sex,
sibsp, parch, fare) %>%
mutate(embarked = factor(embarked),
sex = factor(sex))
## Parsed with column specification:
## cols(
## pclass = col_character(),
## survived = col_integer(),
## name = col_character(),
## sex = col_character(),
## age = col_double(),
## sibsp = col_integer(),
## parch = col_integer(),
## ticket = col_character(),
## fare = col_double(),
## cabin = col_character(),
## embarked = col_character(),
## boat = col_character(),
## body = col_integer(),
## home.dest = col_character()
## )
#load("/Users/robertness/Downloads/titanic.Rdata")
Split data into training and test sets
.data <- c("training", "test") %>%
sample(nrow(titanic3), replace = T) %>%
split(titanic3, .)
Recursive partitioning is implemented in “rpart” package
rtree_fit <- rpart(survived ~ .,
.data$training)
rpart.plot(rtree_fit)
Conditional partitioning is implemented in the “ctree” method
tree_fit <- ctree(survived ~ .,
data = .data$training)
plot(tree_fit)
Use ROCR package to visualize ROC Curve and compare methods
tree_roc <- tree_fit %>%
predict(newdata = .data$test) %>%
prediction(.data$test$survived) %>%
performance("tpr", "fpr")
plot(tree_roc)
Acknowledgement: the code for this excersise is from http://bit.ly/2fqWKvK