R 简明教程
R - Decision Tree
Decision tree is a graph to represent choices and their results in form of a tree. The nodes in the graph represent an event or choice and the edges of the graph represent the decision rules or conditions. It is mostly used in Machine Learning and Data Mining applications using R.
Examples of use of decision tress is − predicting an email as spam or not spam, predicting of a tumor is cancerous or predicting a loan as a good or bad credit risk based on the factors in each of these. Generally, a model is created with observed data also called training data. Then a set of validation data is used to verify and improve the model. R has packages which are used to create and visualize decision trees. For new set of predictor variable, we use this model to arrive at a decision on the category (yes/No, spam/not spam) of the data.
The R package "party" is used to create decision trees.
Install R Package
在 R 控制台中使用以下命令安装包。还需要安装相关包（如果存在）。
install.packages("party")The package "party" has the function ctree() which is used to create and analyze decison tree.
Syntax
The basic syntax for creating a decision tree in R is −
ctree(formula, data)以下是所用参数的描述 -
-
formula 是描述预测变量和响应变量的公式。
-
data 是所用数据集的名称。
Input Data
We will use the R in-built data set named readingSkills to create a decision tree. It describes the score of someone’s readingSkills if we know the variables "age","shoesize","score" and whether the person is a native speaker or not.
以下是示例数据。
# Load the party package. It will automatically load other
# dependent packages.
library(party)
# Print some records from data set readingSkills.
print(head(readingSkills))当我们执行上述代码时，它会产生以下结果和图表：
nativeSpeaker age shoeSize score
1 yes 5 24.83189 32.29385
2 yes 6 25.95238 36.63105
3 no 11 30.42170 49.60593
4 yes 7 28.66450 40.28456
5 yes 11 31.88207 55.46085
6 yes 10 30.07843 52.83124
Loading required package: methods
Loading required package: grid
...............................
...............................Example
We will use the ctree() function to create the decision tree and see its graph.
# Load the party package. It will automatically load other
# dependent packages.
library(party)
# Create the input data frame.
input.dat <- readingSkills[c(1:105),]
# Give the chart file a name.
png(file = "decision_tree.png")
# Create the tree.
output.tree <- ctree(
nativeSpeaker ~ age + shoeSize + score,
data = input.dat)
# Plot the tree.
plot(output.tree)
# Save the file.
dev.off()当我们执行上述代码时，会产生以下结果 -
null device
1
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