R语言数据建模流程分析
intro
近期在整理数据分析流程，找到了之前写的一篇代码，分享给大家。这是我上学时候做的一个项目，当时由于经验不足产生了一些问题，这些问题会在之后一点一点给大家讨论，避免各位踩坑。本篇分享会带一些讲解，可能有些地方不够清楚，欢迎留言讨论。
本次除了分享之外也是对自己之前项目的一个复盘。还是使用r语言（毕竟是我钟爱的语言）。python的如果有需求之后会放别的项目。
本篇中包含了数据导入，清洗，可视化，特征工程，建模的代码，大家可以选择需要的去参考。
项目背景
数据来自online shopper’s intention 包含12,330 条数据, 10个计数型特征和8个类别型特征。 使用‘revenue’ 作为标签进行建模。最终目的就是根据拿到的这些数据去建立一个可以预测revenue的模型。
前期准备
首先你要下载一个r语言以及它的舒适版本r studio。怎么下载呢，把我之前文章上的话直接粘过来哈哈
安装r以及rstudio
如果之前有用过r的朋友请忽略这一段。
安装r非常简单，直接官网下载
之后下载rstudio，这个相当于r语言的开挂版，界面相比于r来说非常友好，辅助功能也很多，下载地址
#注意rstudio是基于r语言的，需要下载安装r语言后才可以安装使用。
安装好了后运行以下代码来导入package们。
setwd("~/desktop/stat5003/ass") #选择项目存放的位置，同样这也是你数据csv存放的位置
# install.packages("xxx") 如果之前没有装过以下的包，先用这句话来装包，然后再去load
# the following packages are for the eda part
library(ggally)
library(ggcorrplot)
library(psych)
library(ggstatsplot)
library(ggplot2)
library(grid)
# the following packages are for the model part
library(mass)
library(boruta) # feature selection with the boruta algorithm
library(caret)
library(mlmetrics)
library(class)
library(neuralnet)
library(e1071)
library(randomforest)
library(keras)
导入的包有些多，keras那个的安装可以参考我之前的文章 （r语言基于keras的mlp神经网络详解
）
数据描述
首先啊把这个数据下载到你的电脑上，然后用以下代码导入r就可以了。
dataset <- read.csv("online_shoppers_intention.csv")
str(dataset)
str（）这个function可以看到你这个数据的属性，输出如下：
此时发现数据格式有int，number，factor等等。为了之后建分析和建模方便，我们先统一数据格式。
dataset$operatingsystems <- as.factor(dataset$operatingsystems) dataset$browser <- as.factor(dataset$browser) dataset$region <- as.factor(dataset$region) dataset$traffictype <- as.factor(dataset$traffictype) dataset$weekend <- as.factor(dataset$weekend) dataset$revenue <- as.factor(dataset$revenue) dataset$administrative <- as.numeric(dataset$administrative) dataset$informational <- as.numeric(dataset$informational) dataset$productrelated <- as.numeric(dataset$productrelated) summary(dataset)
现在数据格式基本统一啦，分为factor和numeric，这方便我们之后的操作。因为r里面的一些package（尤其是建模的package）对数据的输入格式有要求，所以提前处理好非常重要。这可以帮助你更好的整理数据以及敲出简洁舒爽的代码。
记住整理好数据格式之后summary（）一下，你可以从这里发现一些数据的小问题。比如下面的这个‘administrative_duration ’。
你看这min=-1就离谱，（当然这也是一个小坑）我们知道duration不可能是<0的。但这是我们的主观思维，由于不知道这个数据在采集入数据库的时候是怎么定义的，所以这个-1是为啥我们不会知道原因。这也是为什么我推荐做数据分析的时候要从头开始跟项目，这样你对数据了如指掌，而不是像现在这样只凭主观思想去判断数据对错（虽然大部分时候你的主观思想没啥问题）
以下给一些数据解释，就不翻译了，看或不看都可（但你自己做项目的时候一定一定一定要仔细看）
variables are described as follows：
administrative : administrative value
administrative_duration : duration in administrative page
informational : informational value
informational_duration : duration in informational page
productrelated : product related value
productrelated_duration : duration in product related page
bouncerates : bounce rates of a web page
exitrates : exit rate of a web page
pagevalues : page values of each web page
specialday : special days like valentine etc
month : month of the year
operatingsystems : operating system used
browser : browser used
region : region of the user
traffictype : traffic type
visitortype : types of visitor
weekend : weekend or not
revenue : revenue will be generated or not
数据清洗
我们在上一部分的summary已经发现了duration有小于0的，因此所有小于0的duration相关的，我们把它变成na，然后算一下na率，来判断这些数是给它填补上还是直接删。个人认为如果missing rate很小删了就成。但如果你的数据集本身就不大，那建议你使用填值法填进去。因为数据太少的话就没啥分析的必要。具体多少算少，见仁见智吧，感兴趣的话之后可以写一篇做讨论。
dataset$administrative_duration[dataset$administrative_duration < 0] = na
dataset$informational_duration[dataset$informational_duration < 0] = na
dataset$productrelated_duration[dataset$productrelated_duration < 0] = na
missing.rate <- 1 - nrow(na.omit(dataset))/nrow(dataset)
paste("missing rate =", missing.rate * 100, "%")"missing rate = 0.381184103811838 %"还挺小的，所以直接删掉有问题的数据。
dataset <- na.omit(dataset)
然后记得用summary再查一次哦，看看是否删干净了。
预分析及预处理
数值型数据
下面三种分别是箱形图，ggpairs以及相关性矩阵。 箱形图可以用来观察数据整体的分布情况。ggpairs绘制的相关关系图可以查看数据分布和相关性。相关性矩阵专注于看相关系数以及是否相关性是否significant。这几个各有其注重点，根据需要去做就可以。
par(mfrow = c(2, 5)) #让图片以2行5列的形式排列在一张图上 boxplot(dataset$administrative, main = "administrative") boxplot(dataset$administrative_duration, main = "administrative_duration") boxplot(dataset$informational, main = "informational") boxplot(dataset$informational_duration, main = "informational_duration") boxplot(dataset$productrelated, main = "productrelated") boxplot(dataset$productrelated_duration, main = "productrelated_duration") boxplot(dataset$bouncerates, main = "bouncerates") boxplot(dataset$exitrates, main = "exitrates") boxplot(dataset$pagevalues, main = "pagevalues") boxplot(dataset$specialday, main = "specialday")
ggpairs(dataset[, c(1:10)])
corr = cor(dataset[, c(1:10)])
p.mat <- cor_pmat(dataset[, c(1:10)], use = "complete", method = "pearson")
ggcorrplot(corr, hc.order = true, type = "lower", lab = true, p.mat = p.mat,
insig = "blank")类别型数据
针对类别型数据我们主要是看他的分布，因此直接画bar plot就成。下面的代码用到了ggplot，是个非常好用的可视化包。grid.newpage()这里主要是为了让这些图片都显示在一张图上，这样把图片导出或是直接在markdown上显示的时候所有图都会显示在一个页面上面，看起来比较美观和舒适。
p1 <- ggplot(dataset, aes(x = specialday)) + geom_bar(fill = "#cf6a1a", colour = "black") +
theme_bw()
p2 <- ggplot(dataset, aes(x = month)) + geom_bar(fill = "#cf6a1a", colour = "black") +
theme_bw()
p3 <- ggplot(dataset, aes(x = operatingsystems)) + geom_bar(fill = "#cf6a1a",
colour = "black") + theme_bw()
p4 <- ggplot(dataset, aes(x = browser)) + geom_bar(fill = "#cf6a1a", colour = "black") +
theme_bw()
p5 <- ggplot(dataset, aes(x = region)) + geom_bar(fill = "#cf6a1a", colour = "black") +
theme_bw()
p6 <- ggplot(dataset, aes(x = traffictype)) + geom_bar(fill = "#cf6a1a", colour = "black") +
theme_bw()
p7 <- ggplot(dataset, aes(x = visitortype)) + geom_bar(fill = "#cf6a1a", colour = "black") +
theme_bw()
p8 <- ggplot(dataset, aes(x = weekend)) + geom_bar(fill = "#cf6a1a", colour = "black") +
theme_bw()
p9 <- ggplot(dataset, aes(x = revenue)) + geom_bar(fill = "#cf6a1a", colour = "black") +
theme_bw()
grid.newpage()
pushviewport(viewport(layout = grid.layout(4, 3, heights = unit(c(1, 3, 3, 3),
"null"))))
grid.text("bar plot of all categorical feature", vp = viewport(layout.pos.row = 1,
layout.pos.col = 1:3))
vplayout = function(x, y) viewport(layout.pos.row = x, layout.pos.col = y)
print(p1, vp = vplayout(2, 1))
print(p2, vp = vplayout(2, 2))
print(p3, vp = vplayout(2, 3))
print(p4, vp = vplayout(3, 1))
print(p5, vp = vplayout(3, 2))
print(p6, vp = vplayout(3, 3))
print(p7, vp = vplayout(4, 1))
print(p8, vp = vplayout(4, 2))
print(p9, vp = vplayout(4, 3))
我们可以看到，数据还是比较偏。我们想要预测的revenue也是非常imbalance（标签中的false与true占比不均衡）。因此在处理数据或是选择模型的时候要注意这一点。这里不作详细讨论。针对imbalance data应该是有很多可以说的东西。之后有空的话可以细聊~
其实到目前为止，作为一个普通的项目来说，预分析可以结束了，我们查看了所有数据的分布，并且对现有的数据有了一些直观的印象。但我们不能满足于此，因此对每一个类别型变量再做一次更细致的分析。
首先看一下这个 special day 。原数据里给的这个special day给的是0，0.2，0.4这种数值，代表的是距离节日当天的日子，比如1就是节日当天，0.2是节日的前几天（我记得大概是这样）但这种就比较迷惑，我不知道这个具体是咋划分的（这也是为啥希望大家对你所研究的项目有非常深入的了解，你如果对此很了解，那么很多分析的步骤是可以省略的），所以只能让数据告诉我，special day应该如何存在于我们之后的模型中。
special_day_check <- dataset[, c(10, 18)]
special_day_check$revenue <- ifelse(special_day_check$revenue == "false", 0,
1)
special_day_check$specialday[special_day_check$specialday == 0] = na
special_day_check <- na.omit(special_day_check)
special_day_glm <- glm(revenue ~ specialday, data = special_day_check, family = binomial(link = "logit"))
summary(special_day_glm)
##
## call:
## glm(formula = revenue ~ specialday, family = binomial(link = "logit"),
## data = special_day_check)
##
## deviance residuals:
## min 1q median 3q max
## -0.3961 -0.3756 -0.3560 -0.3374 2.4491
##
## coefficients:
## estimate std. error z value pr(>|z|)
## (intercept) -2.3954 0.2986 -8.021 1.05e-15 ***
## specialday -0.5524 0.4764 -1.159 0.246
## ---
## signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (dispersion parameter for binomial family taken to be 1)
##
## null deviance: 578.11 on 1247 degrees of freedom
## residual deviance: 576.77 on 1246 degrees of freedom
## aic: 580.77
##
## number of fisher scoring iterations: 5
首先，我们要检查的是special day 是否应该是一个数值变量。因此，建立一个glm模型（revenue = a+b*special_day)，发现special day的p值=0.246（>0.05），因此可以数值型的认为“specialday”不对revenue有显著的影响，因此specialday可以被当作类别型变量。
现在我们把它当作类别型变量分析一下。用ggbarstats这个function。ggstatsplot是ggplot2包的扩展，主要用于创建美观的图片同时自动输出统计学分析结果，其统计学分析结果包含统计分析的详细信息，该包对于经常需要做统计分析的科研工作者来说非常有用。
ggbarstats(data = dataset, main = revenue, condition = specialday, sampling.plan = "jointmulti",
title = "revenue by special days", xlab = "special days", perc.k = 0.5,
x.axis.orientation = "slant", ggstatsplot.layer = false, messages = false)
用此函数可以绘制出呈现分类变量的柱状图，图中的上半部分( x p e a r s o n 2 x^2_{pearson} xpearson2, p p p , v c r a m e r v_{cramer} vcramer 等)代表传统的统计学方法（frequentist）的一些统计值，下面的部分( l o g e ( b f 01 ) log_e(bf_{01}) loge(bf01)等)代表贝叶斯（bayesian）的一些统计值。
在本项目中，我们主要关注p-value，我们发现，p<0.001并且在柱状图上方所有都是***，这代表了非常显著。因此我们可以确定special day就这样作为类别型变量使用。
之后把每一个类别型变量都这样做一下。过程不赘述了，挑一个有代表性的给大家看一下。
我们看一下operating systems的ggbarstats（）。
ggbarstats(data = dataset, main = revenue, condition = operatingsystems, sampling.plan = "jointmulti",
title = "revenue by different operating systems", xlab = "operating systems",
perc.k = 0.5, x.axis.orientation = "slant", ggstatsplot.layer = false, messages = false)
我们发现整体的p<0.001但是，因为在子类别的样本少，所以柱状图上面出现了ns。我们知道，如果数据很少，那么该数据便不具有统计价值，因此我们把这些少样本的子类别合并在一起，再看一次。
dataset$operatingsystems <- as.integer(dataset$operatingsystems)
dataset$operatingsystems[dataset$operatingsystems == "5"] <- "other"
dataset$operatingsystems[dataset$operatingsystems == "6"] <- "other"
dataset$operatingsystems[dataset$operatingsystems == "7"] <- "other"
dataset$operatingsystems <- as.factor(dataset$operatingsystems)
ggbarstats(data = dataset, main = revenue, condition = operatingsystems, sampling.plan = "jointmulti",
title = "revenue by different operating systems", xlab = "operating systems",
perc.k = 0.5, x.axis.orientation = "slant", ggstatsplot.layer = false, messages = false)现在看起来就比较舒适了，都很显著。
预处理和预分析到此结束。
特征
我们进行特征工程的最终目的就是提升模型的性能，比如你的数据特征很少的话我们需要建立一些二阶、三阶特征来丰富我们的数据。或是特征太多的时候我们需要进行降维处理。这里我没有做太多的特征工程，只是把特征进行了一下基本的筛选，把没有用的特征删掉。这里的逻辑是先用pca看一下可以保留多少特征，再用boruta算法和stepaic去选一下。
# pca since pca can only use on numeric data, so we use the os[,c(1:9)] pcdata <- os[, c(1:9)] pclable <- ifelse(os$revenue == "true", "red", "blue") pc <- princomp(os[, c(1:9)], cor = true, scores = true) summary(pc) ## importance of components: ## comp.1 comp.2 comp.3 comp.4 comp.5 ## standard deviation 1.8387377 1.2923744 1.0134790 1.0020214 0.9697619 ## proportion of variance 0.3756618 0.1855813 0.1141266 0.1115608 0.1044931 ## cumulative proportion 0.3756618 0.5612431 0.6753697 0.7869305 0.8914236 ## comp.6 comp.7 comp.8 comp.9 ## standard deviation 0.65008195 0.59319914 0.3510795 0.281849096 ## proportion of variance 0.04695628 0.03909836 0.0136952 0.008826546 ## cumulative proportion 0.93837989 0.97747825 0.9911735 1.000000000 plot(pc, type = "lines")
从pca里面我们可以发现，保留7个numeric变量就可以有95%以上的方差。因此之后我们可以按着至少7个numeric variable这个标准去保留。
boruta算法
set.seed(123) boruta.train <- boruta(revenue ~ ., data = os, dotrace = 2, maxruns = 15) print(boruta.train) # boruta performed 14 iterations in 3.920271 mins. 13 attributes confirmed # important: administrative, administrative_duration, bouncerates, browser, # exitrates and 8 more; 1 attributes confirmed unimportant: specialday; 2 # tentative attributes left: operatingsystems, weekend; so specialday can be # delete when we fit the model. operatingsystems and weekend need to check # by other ways.
stepaic
full.model <- glm(revenue ~ . - specialday, data = os, family = binomial(link = "logit")) # backward stepwise aic stepback <- stepaic(full.model, direction = "backward", steps = 3) summary(stepback) # operatingsystems, weekend are all above the <none>, combine the previous # result by boruta algorithm, it can be delete when we fit model. browser # has the minimum aic, it can be delete when we fit model. pca shows we # should keep 7 numeric variables in the dataset when fit the model, so two # numeric variables should be remove. informational_duration and # administrative has the minimum aic in numeric variables, so remove these # two variables.
综合上面三个特征选择的方法 specialday, operatingsystems, weekend, browser, informational_duration 和 administrative 应当在建模的时候被移除。有兴趣的可以跑一下上面的代码，由于运行时间有点长，结果就直接码在代码框里了。
建模
现在把用来建模数据整理好，准备建模。
os_modeldata <- os[, -c(1, 4, 10, 11, 12, 16)] # summary(os_modeldata) write.csv(os_modeldata, "os_modeldata.csv")
首先划分训练集和测试集（train 和 test）
set.seed(123)
os_modeldata <- read.csv("os_modeldata.csv")
os_modeldata <- os_modeldata[, -1]
os_modeldata$revenue <- as.factor(os_modeldata$revenue)
intrain <- createdatapartition(os_modeldata$revenue, p = 0.9)[[1]]
train <- os_modeldata[intrain, ]
test <- os_modeldata[-intrain, ]然后把训练集拆成train和val。这里加了个10-cv。有些模型的function可以自己加cv，但由于要用到不同的建模package，为了避免不同package之间划分cv的差异，咱自己建~
add_cv_cohorts <- function(dat, cv_k) {
if (nrow(dat)%%cv_k == 0) {
# if perfectly divisible
dat$cv_cohort <- sample(rep(1:cv_k, each = (nrow(dat)%/%cv_k)))
} else {
# if not perfectly divisible
dat$cv_cohort <- sample(c(rep(1:(nrow(dat)%%cv_k), each = (nrow(dat)%/%cv_k +
1)), rep((nrow(dat)%%cv_k + 1):cv_k, each = (nrow(dat)%/%cv_k))))
}
return(dat)
}
# add 10-fold cv labels to real estate data
train_cv <- add_cv_cohorts(train, 10)
# str(train_cv)首先建一个基准模型，logistic regression classifer（benchmark model）
train_cv_glm <- train_cv
glm.acc <- glm.f1 <- c()
train_cv_glm$revenue <- ifelse(train_cv_glm$revenue == "true", 1, 0)
# str(train_cv_glm)
for (i in 1:10) {
# segement my data by fold using the which() function
indexes <- which(train_cv_glm$cv_cohort == i)
train <- train_cv_glm[-indexes, ]
val <- train_cv_glm[indexes, ]
# model
glm.model <- glm(revenue ~ . - cv_cohort, data = train, family = binomial(link = "logit"))
# predict
glm.pred <- predict(glm.model, newdata = val, type = "response")
glm.pred <- ifelse(glm.pred > 0.5, 1, 0)
# evaluate
glm.f1[i] <- f1_score(val$revenue, glm.pred, positive = "1")
glm.acc[i] <- sum(glm.pred == val$revenue)/nrow(val)
}
# f1 and acc
glm.acc.train <- round(mean(glm.acc), 5) * 100
glm.f1.train <- round(mean(glm.f1), 5) * 100
# print(glm.cm <- table(glm.pred, val$revenue))
paste("the accuracy by logistic regression classifier by 10-fold cv in train data is",
glm.acc.train, "%")
paste("the f1-score by logistic regression classifier by 10-fold cv in train data is",
glm.f1.train, "%")
# f1 = 0.50331然后建立我们用来对比的机器学习模型。这里使用网格搜索法调参。
knn
# since knn() function can't use factor as indenpent variable so re-coding
# data, factor to dummy variable)
train_cv_knn <- as.data.frame(model.matrix(~., train_cv[, -11]))
train_cv_knn$revenue <- train_cv$revenue
train_cv_knn <- train_cv_knn[, -1]
# head(train_cv_knn)
knn.grid <- expand.grid(k = c(1:30))
knn.grid$acc <- knn.grid$f1 <- na
knn.f1 <- knn.acc <- c()
for (k in 1:nrow(knn.grid)) {
for (i in 1:10) {
# segement my data by fold using the which() function
indexes <- which(train_cv_knn$cv_cohort == i)
train <- train_cv_knn[-indexes, ]
val <- train_cv_knn[indexes, ]
# model and predict
knn.pred <- knn(train[, -c(34, 35)], val[, -c(34, 35)], train$revenue,
k = k)
# evaluate
knn.f1[i] <- f1_score(val$revenue, knn.pred, positive = "true")
knn.acc[i] <- sum(knn.pred == val$revenue)/nrow(val)
}
knn.grid$f1[k] <- mean(knn.f1)
knn.grid$acc[k] <- mean(knn.acc)
print(paste("finished with =", k))
}
print(knn.cm <- table(knn.pred, val$revenue))
knn.grid[which.max(knn.grid$f1), ]
# k = 7, f1=0.5484112, acc=0.885042svm
svm.grid <- expand.grid(cost = c(0.1, 1, 10), gamma = seq(0.2, 1, 0.2))
svm.grid$acc <- svm.grid$f1 <- na
svm.f1 <- svm.acc <- c()
for (k in 1:nrow(svm.grid)) {
for (i in 1:10) {
# segement my data by fold using the which() function
indexes <- which(train_cv$cv_cohort == i)
train <- train_cv[-indexes, ]
val <- train_cv[indexes, ]
# model
svm.model <- svm(revenue ~ ., kernel = "radial", type = "c-classification",
gamma = svm.grid$gamma[k], cost = svm.grid$cost[k], data = train[,
-12])
svm.pred <- predict(svm.model, val[, -12])
# evaluate
svm.f1[i] <- f1_score(val$revenue, svm.pred, positive = "true")
svm.acc[i] <- sum(svm.pred == val$revenue)/nrow(val)
}
svm.grid$f1[k] <- mean(svm.f1)
svm.grid$acc[k] <- mean(svm.acc)
print(paste("finished with:", k))
}
print(svm.cm <- table(svm.pred, val$revenue))
svm.grid[which.max(svm.grid$f1), ]
# cost=1, gamma=0.2,f1= 0.5900601,acc= 0.8948096random forest
rf.grid <- expand.grid(nt = seq(100, 500, by = 100), mrty = c(1, 3, 5, 7, 10))
rf.grid$acc <- rf.grid$f1 <- na
rf.f1 <- rf.acc <- c()
for (k in 1:nrow(rf.grid)) {
for (i in 1:10) {
# segement my data by fold using the which() function
indexes <- which(train_cv$cv_cohort == i)
train <- train_cv[-indexes, ]
val <- train_cv[indexes, ]
# model
rf.model <- randomforest(revenue ~ ., data = train[, -12], n.trees = rf.grid$nt[k],
mtry = rf.grid$mrty[k])
rf.pred <- predict(rf.model, val[, -12])
# evaluate
rf.f1[i] <- f1_score(val$revenue, rf.pred, positive = "true")
rf.acc[i] <- sum(rf.pred == val$revenue)/nrow(val)
}
rf.grid$f1[k] <- mean(rf.f1)
rf.grid$acc[k] <- mean(rf.acc)
print(paste("finished with:", k))
}
print(rf.cm <- table(rf.pred, val$revenue))
rf.grid[which.max(rf.grid$f1), ]
# nt=200,mtry=3 ,f1 = 0.6330392, acc=0.8960723neural network
nndata <- train
nndata$revenue <- ifelse(nndata$revenue == "true", 1, 0)
train_x <- model.matrix(~., nndata[, -11])
train_x <- train_x[, -1]
train_y <- to_categorical(as.integer(as.matrix(array(nndata[, 11]))), 2)
model <- keras_model_sequential()
# defining model's layers
model %>% layer_dense(units = 30, input_shape = 33, activation = "relu") %>%
layer_dense(units = 40, activation = "relu") %>% layer_dropout(rate = 0.4) %>%
layer_dense(units = 60, activation = "relu") %>% layer_dropout(rate = 0.4) %>%
layer_dense(units = 30, activation = "relu") %>% layer_dropout(rate = 0.4) %>%
layer_dense(units = 2, activation = "sigmoid")
# defining model's optimizer
model %>% compile(loss = "binary_crossentropy", optimizer = "adam", metrics = c("accuracy"))
# metrics: the performance evaluation module provides a series of functions
# for model performance evaluation. we use it to determine when the nn
# should stop train. the ultimate measure of performance is f1.
# check which column in train_y is false
table(train_y[, 1]) # the first column is false
table(train_y[, 1])[[2]]/table(train_y[, 1])[[1]]
# define a dictionary with your labels and their associated weights
weight = list(5.5, 1) # the proportion of false and ture is about 5.5:1
# fitting the model on the training dataset
model %>% fit(train_x, train_y, epochs = 50, validation_split = 0.2, batch_size = 512,
class_weight = weight)
# after epoch = 20, val_loss not descrease and val_acc not increase, so nn
# should stop at epoch = 20模型对比
glm
glmdata <- train
glmdata$revenue <- ifelse(glmdata$revenue == "true", 1, 0)
testglm <- test
testglm$revenue <- ifelse(testglm$revenue == "true", 1, 0)
glm.model.f <- glm(revenue ~ ., data = glmdata, family = binomial(link = "logit"))
glm.pred.f <- predict(glm.model.f, newdata = test, type = "response")
glm.pred.f <- ifelse(glm.pred.f > 0.5, 1, 0)
glm.f1.f <- f1_score(testglm$revenue, glm.pred.f, positive = "1")
paste("the f1-score by logistic regression classifier in test data is", glm.f1.f)knn
knndata <- as.data.frame(model.matrix(~., train[, -11]))
knndata <- knndata[, -1]
knntest <- as.data.frame(model.matrix(~., test[, -11]))
knntest <- knntest[, -1]
knn.model.f.pred <- knn(knndata, knntest, train$revenue, k = 7)
knn.f1.f <- f1_score(test$revenue, knn.model.f.pred, positive = "true")
paste("the f1-score by knn classifier in test data is", knn.f1.f)svm
svm.model.f <- svm(revenue ~ ., kernel = "radial", type = "c-classification",
gamma = 0.2, cost = 1, data = train)
svm.pred.f <- predict(svm.model.f, test)
svm.f1.f <- f1_score(test$revenue, svm.pred.f, positive = "true")
paste("the f1-score by svm classifier in test data is", svm.f1.f)random forests
rf.model.f <- randomforest(revenue ~ ., data = train, n.trees = 200, mtry = 3)
rf.pred.f <- predict(rf.model.f, test)
rf.f1.f <- f1_score(test$revenue, rf.pred.f, positive = "true")
paste("the f1-score by random forests classifier in test data is", rf.f1.f)nn
nndata <- train
nndata$revenue <- ifelse(nndata$revenue == "true", 1, 0)
train_x <- model.matrix(~., nndata[, -11])
train_x <- train_x[, -1]
train_y <- to_categorical(as.integer(as.matrix(array(nndata[, 11]))), 2)
model <- keras_model_sequential()
# defining model's layers
model %>% layer_dense(units = 30, input_shape = 33, activation = "relu") %>%
layer_dense(units = 40, activation = "relu") %>% layer_dropout(rate = 0.4) %>%
layer_dense(units = 60, activation = "relu") %>% layer_dropout(rate = 0.4) %>%
layer_dense(units = 30, activation = "relu") %>% layer_dropout(rate = 0.4) %>%
layer_dense(units = 2, activation = "sigmoid")
# defining model's optimizer
model %>% compile(loss = "binary_crossentropy", optimizer = "adam", metrics = c("accuracy"))
weight = list(5.5, 1)
model %>% fit(train_x, train_y, epochs = 20, batch_size = 512, class_weight = weight)
# test data
testnn <- test
testnn$revenue <- ifelse(testnn$revenue == "true", 1, 0)
test_x <- model.matrix(~., testnn[, -11])
test_x <- test_x[, -1]
nn.pred <- model %>% predict(test_x)
nn.pred <- as.data.frame(nn.pred)
nn.pred$label <- na
nn.pred$label <- ifelse(nn.pred$v2 > nn.pred$v1, "true", "false")
nn.pred$label <- as.factor(nn.pred$label)
nn.f1 <- f1_score(test$revenue, nn.pred$label, positive = "true")
paste("the f1-score by neural network in test data is", nn.f1)看一下结果对比哈，rf和nn的表现较好。最后做个混淆矩阵看一下。
# rf print(rf.cm.f <- table(rf.pred.f, test$revenue)) ## ## rf.pred.f false true ## false 987 74 ## true 50 116 # nn print(nn.cm.f <- table(nn.pred$label, test$revenue)) ## ## false true ## false 980 69 ## true 57 121
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