R 编程中的决策树分类器

分类是使用类的属性将几个类别的对象分类到各自的类中的任务。分类模型通常用于，

• 预测新的未标记数据对象的类标签
• 提供一个描述性模型，解释每个类中对象的特征

有各种类型的分类技术，例如，

• 逻辑回归
• 决策树
• K-最近邻
• 朴素贝叶斯分类器
• 支持向量机 (SVM)
• 随机森林分类

R 编程中的决策树分类器

决策树是一种类似流程图的树结构，其中内部节点代表特征（或属性），分支代表决策规则，每个叶节点代表结果。决策树包括：

• 节点：测试某个属性的值。
• Edges/Branch：代表一个决策规则并连接到下一个节点。
• 叶节点：代表类标签或类分布的终端节点。

并且这个算法可以很容易地用 R 语言实现。关于决策树分类器的一些要点是，

• 更易解释
• 自动处理决策
• 将空间分成更小的空间
• 容易过拟合
• 可以在小型训练集上进行训练
• 主要受噪音影响

R中的实现

数据集：

一个 400 人的样本人口与一家产品公司分享了他们的年龄、性别和薪水，以及他们是否购买了该产品（0 表示不，1 表示是）。下载数据集 Advertisement.csv。

R

# Importing the dataset
dataset = read.csv('Advertisement.csv')
head(dataset, 10)

输出：

	User ID	Gender	Age	EstimatedSalary	Purchased
0	15624510	Male	19	19000	0
1	15810944	Male	35	20000	0
2	15668575	Female	26	43000	0
3	15603246	Female	27	57000	0
4	15804002	Male	19	76000	0
5	15728773	Male	27	58000	0
6	15598044	Female	27	84000	0
7	15694829	Female	32	150000	1
8	15600575	Male	25	33000	0
9	15727311	Female	35	65000	0

训练数据

为了训练数据，我们将数据集拆分为测试集，然后使用 rpart 包制作决策树分类器。

R

# Encoding the target feature as factor
dataset$Purchased = factor(dataset$Purchased,
                           levels = c(0, 1))
 
# Splitting the dataset into
# the Training set and Test set
# install.packages('caTools')
library(caTools)
set.seed(123)
split = sample.split(dataset$Purchased,
                     SplitRatio = 0.75)
training_set = subset(dataset, split == TRUE)
test_set = subset(dataset, split == FALSE)
 
# Feature Scaling
training_set[-3] = scale(training_set[-3])
test_set[-3] = scale(test_set[-3])
 
# Fitting Decision Tree Classification
# to the Training set
# install.packages('rpart')
library(rpart)
classifier = rpart(formula = Purchased ~ .,
                   data = training_set)
 
# Predicting the Test set results
y_pred = predict(classifier,
                 newdata = test_set[-3],
                 type = 'class')
 
# Making the Confusion Matrix
cm = table(test_set[, 3], y_pred)

• 训练集包含 300 个条目。
• 测试集包含 100 个条目。

Confusion Matrix:
[[62,  6],
 [ 3, 29]]

可视化训练数据：

R

# Visualising the Training set results
# Install ElemStatLearn if not present
# in the packages using(without hashtag)
# install.packages('ElemStatLearn')
library(ElemStatLearn)
set = training_set
 
# Building a grid of Age Column(X1)
# and Estimated Salary(X2) Column
X1 = seq(min(set[, 1]) - 1,
         max(set[, 1]) + 1,
         by = 0.01)
X2 = seq(min(set[, 2]) - 1,
         max(set[, 2]) + 1,
         by = 0.01)
grid_set = expand.grid(X1, X2)
 
# Give name to the columns of matrix
colnames(grid_set) = c('Age',
                       'EstimatedSalary')
 
# Predicting the values and plotting them
# to grid and labelling the axes
y_grid = predict(classifier,
                 newdata = grid_set,
                 type = 'class')
plot(set[, -3],
     main = 'Decision Tree
             Classification (Training set)',
     xlab = 'Age', ylab = 'Estimated Salary',
     xlim = range(X1), ylim = range(X2))
contour(X1, X2, matrix(as.numeric(y_grid),
                       length(X1),
                       length(X2)),
                       add = TRUE)
points(grid_set, pch = '.',
       col = ifelse(y_grid == 1,
                    'springgreen3',
                    'tomato'))
points(set, pch = 21, bg = ifelse(set[, 3] == 1,
                                  'green4',
                                  'red3'))

输出：

可视化测试数据：

R

# Visualising the Test set results
library(ElemStatLearn)
set = test_set
 
# Building a grid of Age Column(X1)
# and Estimated Salary(X2) Column
X1 = seq(min(set[, 1]) - 1,
         max(set[, 1]) + 1,
         by = 0.01)
X2 = seq(min(set[, 2]) - 1,
         max(set[, 2]) + 1,
         by = 0.01)
grid_set = expand.grid(X1, X2)
 
# Give name to the columns of matrix
colnames(grid_set) = c('Age',
                       'EstimatedSalary')
 
# Predicting the values and plotting them
# to grid and labelling the axes
y_grid = predict(classifier,
                 newdata = grid_set,
                 type = 'class')
plot(set[, -3], main = 'Decision Tree
                        Classification (Test set)',
     xlab = 'Age', ylab = 'Estimated Salary',
     xlim = range(X1), ylim = range(X2))
contour(X1, X2, matrix(as.numeric(y_grid),
                       length(X1),
                       length(X2)),
                       add = TRUE)
points(grid_set, pch = '.',
       col = ifelse(y_grid == 1,
                    'springgreen3',
                    'tomato'))
points(set, pch = 21, bg = ifelse(set[, 3] == 1,
                                  'green4',
                                  'red3'))

输出：

决策树图：

R

# Plotting the tree
plot(classifier)
text(classifier)

输出：