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model_rf.Rmd
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model_rf.Rmd
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## Building the model using random forest classifier
```{r}
library(randomForest)
library(caret)
library(e1071)
```
## creating one more temporary table called kddcopy modifying result column of that to labels
#number of attributes: 6 training data size: 1262 , testing data size: 124711
```{r}
data1<-kddcopy[,c("srv_rerror_rate", "rerror_rate", "flag", "dst_host_rerror_rate", "logged_in", "protocol_type","result" )]
inTrain <- createDataPartition(y=data1$result,p=0.01, list=FALSE)
training <- data1[inTrain,]
testing <- data1[-inTrain,]
dim(training)
modFit <- train(result ~ .,method="rf",data=training)
modFit
pred <- predict(modFit,testing)
testing$predRight <- pred==testing$result
answer<-table(pred,testing$result)
round(prop.table(answer,1)*100,2)
```
Output:
>answer
pred dos normal probe r2l u2r
dos 41277 2377 576 0 0
normal 1231 62162 1209 928 51
probe 2959 2130 9754 57 0
r2l 0 0 0 0 0
u2r 0 0 0 0 0
> round(prop.table(answer,1)*100,2)
pred dos normal probe r2l u2r
dos 93.32 5.37 1.30 0.00 0.00
normal 1.88 94.79 1.84 1.42 0.08
probe 19.86 14.30 65.46 0.38 0.00
r2l
u2r
#number of attributes: 5 training data size: 1262 , testing data size: 124711
```{r}
data1<-kddcopy[,c("srv_rerror_rate", "rerror_rate", "flag", "dst_host_rerror_rate", "logged_in","result" )]
inTrain <- createDataPartition(y=data1$result,p=0.01, list=FALSE)
training <- data1[inTrain,]
testing <- data1[-inTrain,]
dim(training)
modFit <- train(result ~ .,method="rf",data=training)
modFit
pred <- predict(modFit,testing)
testing$predRight <- pred==testing$result
answer<-table(pred,testing$result)
round(prop.table(answer,1)*100,2)
```
pred dos normal probe r2l u2r
dos 93.04 5.41 1.55 0.00 0.00
normal 5.44 86.20 7.03 1.26 0.07
probe 3.05 10.85 85.29 0.81 0.00
r2l
u2r
#readin test data
#number of attributes: 6 training data size: 12600 , testing data size: 113373
```{r}
data1<-kddcopy[,c("srv_rerror_rate", "rerror_rate", "flag", "dst_host_rerror_rate", "logged_in","result" )]
inTrain <- createDataPartition(y=data1$result,p=0.1, list=FALSE)
training <- data1[inTrain,]
testing <- data1[-inTrain,]
dim(training)
modFit <- train(result ~ .,method="rf",data=training)
modFit
pred <- predict(modFit,testing)
testing$predRight <- pred==testing$result
answer<-table(pred,testing$result)
round(prop.table(answer,1)*100,2)
```
pred dos normal probe r2l u2r
dos 94.32 5.23 0.45 0.00 0.00
normal 5.23 86.31 7.13 1.26 0.07
probe 6.91 7.76 85.13 0.19 0.02
r2l 0.00 0.00 23.40 76.60 0.00
#number of attributes: 6 training data size: 25197 , testing data size: 100776
```{r}
data1<-kddcopy[,c("srv_rerror_rate", "rerror_rate", "flag", "dst_host_rerror_rate", "logged_in","result" )]
inTrain <- createDataPartition(y=data1$result,p=0.2, list=FALSE)
training <- data1[inTrain,]
testing <- data1[-inTrain,]
dim(training)
modFit <- train(result ~ .,method="rf",data=training)
modFit
pred <- predict(modFit,testing)
testing$predRight <- pred==testing$result
answer<-table(pred,testing$result)
round(prop.table(answer,1)*100,8)
```
TIme taken: 11:50 to 12:18
pred dos normal probe r2l u2r
dos 94.31 5.25 0.44 0.00 0.00
normal 5.13 86.36 7.17 1.26 0.07
probe 7.18 6.18 86.55 0.09 0.00
r2l 0.00 3.64 32.73 63.64 0.00
u2r
#number of attributes: 9 training data size: 12600 , testing data size: 113373
```{r}
data1<-kddcopy[,c("srv_rerror_rate", "protocol_type" ,"src_bytes", "rerror_rate", "flag", "dst_host_rerror_rate", "logged_in","result","service" ,"serror_rate" )]
inTrain <- createDataPartition(y=data1$result,p=0.1, list=FALSE)
training <- data1[inTrain,]
testing <- data1[-inTrain,]
dim(training)
modFit <- train(result ~ .,method="rf",data=training)
modFit
pred <- predict(modFit,testing)
testing$predRight <- pred==testing$result
answer<-table(pred,testing$result)
round(prop.table(answer,1)*100,2)
```
pred dos normal probe r2l u2r
dos 98.92 0.53 0.55 0.00 0.00
normal 0.36 98.86 0.23 0.47 0.07
probe 0.00 2.37 97.54 0.07 0.02
r2l 0.00 6.86 0.00 93.14 0.00
u2r
#number of attributes: 9 training data size: 12625 , testing data size: 124711 and test data as test.final
```{r}
data1<-kddcopy[,c("srv_rerror_rate", "protocol_type" ,"src_bytes", "rerror_rate", "flag", "dst_host_rerror_rate", "logged_in","result","service" ,"serror_rate" )]
inTrain <- createDataPartition(y=data1$result,p=0.1, list=FALSE)
training <- data1[inTrain,]
testing <- data1[-inTrain,]
dim(training)
modFit <- train(result ~ .,method="rf",data=training)
modFit
pred <- predict(modFit,test.final)
testing$predRight <- pred==test.final$result
answer<-table(pred,test.final$result)
round(prop.table(answer,1)*100,2)
```
1262 samples
9 predictor
5 classes: 'dos', 'normal', 'probe', 'r2l', 'u2r'
No pre-processing
Resampling: Bootstrapped (25 reps)
Summary of sample sizes: 1262, 1262, 1262, 1262, 1262, 1262, ...
Resampling results across tuning parameters:
mtry Accuracy Kappa
2 0.8523705 0.7218909
44 0.9785942 0.9623108
87 0.9752067 0.9568197
Accuracy was used to select the optimal model using the largest value.
The final value used for the model was mtry = 44.
> round(prop.table(answer,1)*100,2)
pred dos normal probe r2l u2r
dos 96.82 1.68 1.50 0.00 0.00
normal 0.75 80.21 0.23 18.50 0.31
probe 23.09 12.35 64.49 0.06 0.00
r2l 0.00 10.71 0.00 89.29 0.00
u2r