diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
index bc6eeac1db5da..ba8fbee84197c 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
@@ -214,12 +214,13 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     /*
        Using the following R code to load the data and train the model using glmnet package.
 
-       > library("glmnet")
-       > data <- read.csv("path", header=FALSE)
-       > label = factor(data$V1)
-       > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
-       > weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 0))
-       > weights
+       library("glmnet")
+       data <- read.csv("path", header=FALSE)
+       label = factor(data$V1)
+       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 0))
+       weights
+
        5 x 1 sparse Matrix of class "dgCMatrix"
                            s0
        (Intercept)  2.8366423
@@ -245,13 +246,14 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     /*
        Using the following R code to load the data and train the model using glmnet package.
 
-       > library("glmnet")
-       > data <- read.csv("path", header=FALSE)
-       > label = factor(data$V1)
-       > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
-       > weights =
+       library("glmnet")
+       data <- read.csv("path", header=FALSE)
+       label = factor(data$V1)
+       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       weights =
            coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 0, intercept=FALSE))
-       > weights
+       weights
+
        5 x 1 sparse Matrix of class "dgCMatrix"
                            s0
        (Intercept)   .
@@ -278,12 +280,13 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     /*
        Using the following R code to load the data and train the model using glmnet package.
 
-       > library("glmnet")
-       > data <- read.csv("path", header=FALSE)
-       > label = factor(data$V1)
-       > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
-       > weights = coef(glmnet(features,label, family="binomial", alpha = 1, lambda = 0.12))
-       > weights
+       library("glmnet")
+       data <- read.csv("path", header=FALSE)
+       label = factor(data$V1)
+       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       weights = coef(glmnet(features,label, family="binomial", alpha = 1, lambda = 0.12))
+       weights
+
        5 x 1 sparse Matrix of class "dgCMatrix"
                             s0
        (Intercept) -0.05627428
@@ -310,13 +313,14 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     /*
        Using the following R code to load the data and train the model using glmnet package.
 
-       > library("glmnet")
-       > data <- read.csv("path", header=FALSE)
-       > label = factor(data$V1)
-       > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
-       > weights = coef(glmnet(features,label, family="binomial", alpha = 1, lambda = 0.12,
+       library("glmnet")
+       data <- read.csv("path", header=FALSE)
+       label = factor(data$V1)
+       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       weights = coef(glmnet(features,label, family="binomial", alpha = 1, lambda = 0.12,
            intercept=FALSE))
-       > weights
+       weights
+
        5 x 1 sparse Matrix of class "dgCMatrix"
                             s0
        (Intercept)   .
@@ -343,12 +347,13 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     /*
        Using the following R code to load the data and train the model using glmnet package.
 
-       > library("glmnet")
-       > data <- read.csv("path", header=FALSE)
-       > label = factor(data$V1)
-       > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
-       > weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 1.37))
-       > weights
+       library("glmnet")
+       data <- read.csv("path", header=FALSE)
+       label = factor(data$V1)
+       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 1.37))
+       weights
+
        5 x 1 sparse Matrix of class "dgCMatrix"
                             s0
        (Intercept)  0.15021751
@@ -375,13 +380,14 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     /*
        Using the following R code to load the data and train the model using glmnet package.
 
-       > library("glmnet")
-       > data <- read.csv("path", header=FALSE)
-       > label = factor(data$V1)
-       > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
-       > weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 1.37,
+       library("glmnet")
+       data <- read.csv("path", header=FALSE)
+       label = factor(data$V1)
+       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 1.37,
            intercept=FALSE))
-       > weights
+       weights
+
        5 x 1 sparse Matrix of class "dgCMatrix"
                             s0
        (Intercept)   .
@@ -408,12 +414,13 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     /*
        Using the following R code to load the data and train the model using glmnet package.
 
-       > library("glmnet")
-       > data <- read.csv("path", header=FALSE)
-       > label = factor(data$V1)
-       > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
-       > weights = coef(glmnet(features,label, family="binomial", alpha = 0.38, lambda = 0.21))
-       > weights
+       library("glmnet")
+       data <- read.csv("path", header=FALSE)
+       label = factor(data$V1)
+       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       weights = coef(glmnet(features,label, family="binomial", alpha = 0.38, lambda = 0.21))
+       weights
+
        5 x 1 sparse Matrix of class "dgCMatrix"
                             s0
        (Intercept)  0.57734851
@@ -440,13 +447,14 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     /*
        Using the following R code to load the data and train the model using glmnet package.
 
-       > library("glmnet")
-       > data <- read.csv("path", header=FALSE)
-       > label = factor(data$V1)
-       > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
-       > weights = coef(glmnet(features,label, family="binomial", alpha = 0.38, lambda = 0.21,
+       library("glmnet")
+       data <- read.csv("path", header=FALSE)
+       label = factor(data$V1)
+       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       weights = coef(glmnet(features,label, family="binomial", alpha = 0.38, lambda = 0.21,
            intercept=FALSE))
-       > weights
+       weights
+
        5 x 1 sparse Matrix of class "dgCMatrix"
                             s0
        (Intercept)   .
@@ -503,12 +511,13 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     /*
        Using the following R code to load the data and train the model using glmnet package.
 
-       > library("glmnet")
-       > data <- read.csv("path", header=FALSE)
-       > label = factor(data$V1)
-       > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
-       > weights = coef(glmnet(features,label, family="binomial", alpha = 1.0, lambda = 6.0))
-       > weights
+       library("glmnet")
+       data <- read.csv("path", header=FALSE)
+       label = factor(data$V1)
+       features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
+       weights = coef(glmnet(features,label, family="binomial", alpha = 1.0, lambda = 6.0))
+       weights
+
        5 x 1 sparse Matrix of class "dgCMatrix"
                             s0
        (Intercept) -0.2480643