# How to improve accuracy further for forest cover prediction

I am doing Forest-Cover-Type-prediction on Kaggle

this is the train and test data

> dim(train)
[1] 15120    56
> dim(test)
[1] 565892     56


So far I have done this :

combined this data comb <- rbind(train,test)

then mapped 40 mutually exclusive Soil_Type columns into one and 4 mutually exclusive Wilderness_Area column into one.

comb$Soil <- apply(comb[grep("Soil_Ty+",colnames(comb))], 1 , function(x){which(x == 1)}) comb$Wilderness <- apply(comb[grep("Wilderness_Are+",colnames(comb))], 1 , function(x){which(x == 1)})


after deleting these 44 columns I got it down to 14 columns.

> dim(comb)
[1] 581012     14
> colnames(comb)
[1] "Id"                                 "Elevation"                          "Aspect"
[4] "Slope"                              "Horizontal_Distance_To_Hydrology"   "Vertical_Distance_To_Hydrology"
[13] "Soil"                               "Wilderness"


then I separated the data

train <- comb[1:15120,]
test <- comb[15121:581012,]


and ran randomForest on this data

set.seed(415)
fit <- randomForest(Cover_Type ~ .,data=train[-1], importance=TRUE, ntree=2000, na.action = na.omit)
varImpPlot(fit)
predi <- predict(fit,test)


and got an accuracy of 0.70372 on Kaggle. Now I have hit the wall. I tried plotting various variables against the Cover_Type(variable to be predicted) but couldn't do figure out what to do with those.

How do I improve accuracy from here? what is the general approach in such cases?