#install.packages("randomForest")
library(randomForest)
## randomForest 4.6-12
## Type rfNews() to see new features/changes/bug fixes.
library(ISLR)
#View(as.data.frame(Smarket))
table(Smarket$Year)
##
## 2001 2002 2003 2004 2005
## 242 252 252 252 252
train<-Smarket[Smarket$Year<2004,]
test<-Smarket[Smarket$Year>=2004,]
fit<-randomForest(Direction~Lag1+Lag2+Lag3,data=train,
ntree=100,importance=T)
test.pred<-predict(fit,test,type='class')
table(test.pred,test$Direction)
##
## test.pred Down Up
## Down 111 134
## Up 112 147
library(caret)
## Loading required package: lattice
## Loading required package: ggplot2
##
## Attaching package: 'ggplot2'
## The following object is masked from 'package:randomForest':
##
## margin
confusionMatrix(test$Direction,test.pred)
## Confusion Matrix and Statistics
##
## Reference
## Prediction Down Up
## Down 111 112
## Up 134 147
##
## Accuracy : 0.5119
## 95% CI : (0.4673, 0.5564)
## No Information Rate : 0.5139
## P-Value [Acc > NIR] : 0.5533
##
## Kappa : 0.0207
## Mcnemar's Test P-Value : 0.1806
##
## Sensitivity : 0.4531
## Specificity : 0.5676
## Pos Pred Value : 0.4978
## Neg Pred Value : 0.5231
## Prevalence : 0.4861
## Detection Rate : 0.2202
## Detection Prevalence : 0.4425
## Balanced Accuracy : 0.5103
##
## 'Positive' Class : Down
##