在 R 中构建分类模型

2019-11-18 08:00:00 · 飞浪

分类模型有助于预测客户是否会流失、银行贷款是否会违约等。使用 R 来构建和训练你的逻辑回归算法。

介绍

构建分类模型是最重要的数据科学用例之一。分类模型是预测分类标签的模型。这方面的一些示例包括预测客户是否会流失或银行贷款是否会违约。在本指南中，您将学习如何在 R 中构建和评估分类模型。我们将训练逻辑回归算法，这是最古老但最强大的分类算法之一。

数据

在本指南中，我们将使用一个虚构的贷款申请人数据集，其中包含 600 个观测值和 10 个变量，如下所述：

Marital_status：申请人是否已婚（“是”）或未婚（“否”）
Is_graduate：申请人是否为毕业生（“是”）或不是（“否”）
收入：申请人的年收入（美元）
Loan_amount：提交申请的贷款金额（美元）
Credit_score：申请人的信用评分是好（“好”）还是不好（“坏”）
Approval_status：贷款申请是否被批准（“是”）或不被批准（“否”）
年龄：申请人的年龄（岁）
性别：申请人是男性（“M”）还是女性（“F”）
投资额：申请人申报的股票和共同基金投资总额（美元）
目的：申请贷款的目的

让我们首先加载所需的库和数据。

      library(plyr)
library(readr)
library(dplyr)
library(caret)

dat <- read_csv("data.csv")
glimpse(dat)
    

输出：

      Observations: 600
Variables: 10
$ Marital_status  <chr> "Yes", "No", "Yes", "No", "Yes", "Yes", "Yes", "Yes", "Yes", "Ye...
$ Is_graduate     <chr> "No", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Y...
$ Income          <int> 30000, 30000, 30000, 30000, 89900, 133300, 136700, 136700, 17320...
$ Loan_amount     <int> 60000, 90000, 90000, 90000, 80910, 119970, 123030, 123030, 15588...
$ Credit_score    <chr> "Satisfactory", "Satisfactory", "Satisfactory", "Satisfactory", ...
$ approval_status <chr> "Yes", "Yes", "No", "No", "Yes", "No", "Yes", "Yes", "Yes", "No"...
$ Age             <int> 25, 29, 27, 33, 29, 25, 29, 27, 33, 29, 25, 29, 27, 33, 29, 30, ...
$ Sex             <chr> "F", "F", "M", "F", "M", "M", "M", "F", "F", "F", "M", "F", "F",...
$ Investment      <int> 21000, 21000, 21000, 21000, 62930, 93310, 95690, 95690, 121240, ...
$ Purpose         <chr> "Education", "Travel", "Others", "Others", "Travel", "Travel", "...
    

输出显示数据集有四个数字（标记为int）和六个字符变量（标记为chr）。我们将使用下面的代码行将它们转换为因子变量。

      names <- c(1,2,5,6,8,10)
dat[,names] <- lapply(dat[,names] , factor)
glimpse(dat)
    

输出：

      Observations: 600
Variables: 10
$ Marital_status  <fct> Yes, No, Yes, No, Yes, Yes, Yes, Yes, Yes, Yes, No, No, Yes, Yes...
$ Is_graduate     <fct> No, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, No, Yes, Yes, Y...
$ Income          <int> 30000, 30000, 30000, 30000, 89900, 133300, 136700, 136700, 17320...
$ Loan_amount     <int> 60000, 90000, 90000, 90000, 80910, 119970, 123030, 123030, 15588...
$ Credit_score    <fct> Satisfactory, Satisfactory, Satisfactory, Satisfactory, Satisfac...
$ approval_status <fct> Yes, Yes, No, No, Yes, No, Yes, Yes, Yes, No, No, No, Yes, No, Y...
$ Age             <int> 25, 29, 27, 33, 29, 25, 29, 27, 33, 29, 25, 29, 27, 33, 29, 30, ...
$ Sex             <fct> F, F, M, F, M, M, M, F, F, F, M, F, F, M, M, M, M, M, M, M, M, M...
$ Investment      <int> 21000, 21000, 21000, 21000, 62930, 93310, 95690, 95690, 121240, ...
$ Purpose         <fct> Education, Travel, Others, Others, Travel, Travel, Travel, Educa...
    

数据分区

我们将在训练数据集上构建模型，并在测试数据集上评估其性能。这称为评估模型性能的保留验证方法。

下面的第一行代码设置了随机种子，以确保结果的可重复性。第二行加载用于数据分区的caTools包，而第三至第五行创建训练和测试数据集。训练数据集包含 70% 的数据（10 个变量的 420 个观测值），而测试数据包含剩余的 30%（10 个变量的 180 个观测值）。

      set.seed(100)
library(caTools)

spl = sample.split(dat$approval_status, SplitRatio = 0.7)
train = subset(dat, spl==TRUE)
test = subset(dat, spl==FALSE)

print(dim(train)); print(dim(test))
    

输出：

      1] 420  10

[1] 180  10

建立、预测和评估模型

为了拟合逻辑回归模型，第一步是实例化算法。这在下面的第一行代码中使用glm()函数完成。第二行打印训练模型的摘要。

      model_glm = glm(approval_status ~ . , family="binomial", data = train)
summary(model_glm)
    

输出：

      Call:
glm(formula = approval_status ~ ., family = "binomial", data = train)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-2.19539  -0.00004   0.00004   0.00008   2.47763  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)    
(Intercept)               6.238e-02  9.052e+03   0.000   1.0000    
Marital_statusYes         4.757e-01  4.682e-01   1.016   0.3096    
Is_graduateYes            5.647e-01  4.548e-01   1.242   0.2144    
Income                    2.244e-06  1.018e-06   2.204   0.0275 *  
Loan_amount              -3.081e-07  3.550e-07  -0.868   0.3854    
Credit_scoreSatisfactory  2.364e+01  8.839e+03   0.003   0.9979    
Age                      -7.985e-02  1.360e-02  -5.870 4.35e-09 ***
SexM                     -5.879e-01  6.482e-01  -0.907   0.3644    
Investment               -2.595e-06  1.476e-06  -1.758   0.0787 .  
PurposeHome               2.599e+00  9.052e+03   0.000   0.9998    
PurposeOthers            -4.172e+01  3.039e+03  -0.014   0.9890    
PurposePersonal           1.577e+00  2.503e+03   0.001   0.9995    
PurposeTravel            -1.986e+01  1.954e+03  -0.010   0.9919    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 524.44  on 419  degrees of freedom
Residual deviance: 166.96  on 407  degrees of freedom
AIC: 192.96

Number of Fisher Scoring iterations: 19
    

上述输出中的重要性代码“***”显示了特征变量的相对重要性。让我们进一步评估模型，首先使用以下代码设置基线准确度。由于目标变量的多数类比例为 0.68，因此基线准确度为 68%。

      #Baseline Accuracy
prop.table(table(train$approval_status))
    

输出：

      No       Yes 
0.3166667 0.6833333
    

现在让我们评估模型在训练和测试数据上的表现，理想情况下，该表现应该优于基线准确度。我们首先使用下面的第一行代码对训练数据生成预测。第二行创建阈值为 0.5 的混淆矩阵，这意味着对于等于或大于 0.5 的概率预测，算法将预测approved_status变量的响应为Yes。第三行使用混淆矩阵打印模型在训练数据上的准确度，准确度为 91%。

然后我们在测试数据上重复此过程，准确率达到了 88%。

      # Predictions on the training set
predictTrain = predict(model_glm, data = train, type = "response")

# Confusion matrix on training data
table(train$approval_status, predictTrain >= 0.5)
(114+268)/nrow(train) #Accuracy - 91%

#Predictions on the test set
predictTest = predict(model_glm, newdata = test, type = "response")

# Confusion matrix on test set
table(test$approval_status, predictTest >= 0.5)
158/nrow(test) #Accuracy - 88%
    

输出：

      # Confusion matrix and accuracy on training data

     FALSE TRUE
  No    114   19
  Yes    19  268

[1] 0.9095238

# Confusion matrix and accuracy on testing data
     FALSE TRUE
  No     44   13
  Yes     9  114

[1] 0.8777778

结论

在本指南中，您学习了使用强大的逻辑回归算法在 R 中构建分类模型的技术。数据的基线准确率为 68%，而训练和测试数据的准确率分别为 91% 和 88%。总体而言，逻辑回归模型在训练和测试数据集上都大大超过了基线准确率，结果非常好。

要了解有关使用 R 进行数据科学的更多信息，请参阅以下指南：

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在 R 中构建分类模型

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Observations: 600 Variables: 10 $ Marital_status <chr> "Yes", "No", "Yes", "No", "Yes", "Yes", "Yes", "Yes", "Yes", "Ye... $ Is_graduate <chr> "No", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Y... $ Income <int> 30000, 30000, 30000, 30000, 89900, 133300, 136700, 136700, 17320... $ Loan_amount <int> 60000, 90000, 90000, 90000, 80910, 119970, 123030, 123030, 15588... $ Credit_score <chr> "Satisfactory", "Satisfactory", "Satisfactory", "Satisfactory", ... $ approval_status <chr> "Yes", "Yes", "No", "No", "Yes", "No", "Yes", "Yes", "Yes", "No"... $ Age <int> 25, 29, 27, 33, 29, 25, 29, 27, 33, 29, 25, 29, 27, 33, 29, 30, ... $ Sex <chr> "F", "F", "M", "F", "M", "M", "M", "F", "F", "F", "M", "F", "F",... $ Investment <int> 21000, 21000, 21000, 21000, 62930, 93310, 95690, 95690, 121240, ... $ Purpose <chr> "Education", "Travel", "Others", "Others", "Travel", "Travel", "...

Observations: 600 Variables: 10 $ Marital_status <fct> Yes, No, Yes, No, Yes, Yes, Yes, Yes, Yes, Yes, No, No, Yes, Yes... $ Is_graduate <fct> No, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, No, Yes, Yes, Y... $ Income <int> 30000, 30000, 30000, 30000, 89900, 133300, 136700, 136700, 17320... $ Loan_amount <int> 60000, 90000, 90000, 90000, 80910, 119970, 123030, 123030, 15588... $ Credit_score <fct> Satisfactory, Satisfactory, Satisfactory, Satisfactory, Satisfac... $ approval_status <fct> Yes, Yes, No, No, Yes, No, Yes, Yes, Yes, No, No, No, Yes, No, Y... $ Age <int> 25, 29, 27, 33, 29, 25, 29, 27, 33, 29, 25, 29, 27, 33, 29, 30, ... $ Sex <fct> F, F, M, F, M, M, M, F, F, F, M, F, F, M, M, M, M, M, M, M, M, M... $ Investment <int> 21000, 21000, 21000, 21000, 62930, 93310, 95690, 95690, 121240, ... $ Purpose <fct> Education, Travel, Others, Others, Travel, Travel, Travel, Educa...

Call: glm(formula = approval_status ~ ., family = "binomial", data = train) Deviance Residuals: Min 1Q Median 3Q Max -2.19539 -0.00004 0.00004 0.00008 2.47763 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 6.238e-02 9.052e+03 0.000 1.0000 Marital_statusYes 4.757e-01 4.682e-01 1.016 0.3096 Is_graduateYes 5.647e-01 4.548e-01 1.242 0.2144 Income 2.244e-06 1.018e-06 2.204 0.0275 * Loan_amount -3.081e-07 3.550e-07 -0.868 0.3854 Credit_scoreSatisfactory 2.364e+01 8.839e+03 0.003 0.9979 Age -7.985e-02 1.360e-02 -5.870 4.35e-09 *** SexM -5.879e-01 6.482e-01 -0.907 0.3644 Investment -2.595e-06 1.476e-06 -1.758 0.0787 . PurposeHome 2.599e+00 9.052e+03 0.000 0.9998 PurposeOthers -4.172e+01 3.039e+03 -0.014 0.9890 PurposePersonal 1.577e+00 2.503e+03 0.001 0.9995 PurposeTravel -1.986e+01 1.954e+03 -0.010 0.9919 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 524.44 on 419 degrees of freedom Residual deviance: 166.96 on 407 degrees of freedom AIC: 192.96 Number of Fisher Scoring iterations: 19

# Predictions on the training set predictTrain = predict(model_glm, data = train, type = "response") # Confusion matrix on training data table(train$approval_status, predictTrain >= 0.5) (114+268)/nrow(train) #Accuracy - 91% #Predictions on the test set predictTest = predict(model_glm, newdata = test, type = "response") # Confusion matrix on test set table(test$approval_status, predictTest >= 0.5) 158/nrow(test) #Accuracy - 88%

# Confusion matrix and accuracy on training data FALSE TRUE No 114 19 Yes 19 268 [1] 0.9095238 # Confusion matrix and accuracy on testing data FALSE TRUE No 44 13 Yes 9 114 [1] 0.8777778