# How to improve accuracy of Decision tree in R studio

I am currently conducting a study on the predictive qualities of odds (Regarding Football/Soccer). I have odds from multiple bookies on each of the seasons and leagues within the study ( as below ). The percentage of correctly predicted matches is rather low. Being between 40-50%, sometimes even 30% and rarely going over 50%. Is there anything wrong with the code or within the data I am providing to the Decision tree that is causing such a low percentage?

I have already tried k-fold cross-validation and adding extra data such as Elo ratings to no avail. I am excluding null values. Teams have been given both as factors and as dummy variables.

Structure of Data

|-----|--------------|---------------|-------|-------|-------|
| FTR |  Home Team   |  Away Team    |  BetH |  BetD |  BetA |
|-----|--------------|---------------|-------|-------|-------|
|  H  |   Chelsea    |   Liverpool   |  1.35 |  3.35 |  2.65 |


R Code

DT1 <- x

set.seed(123)
DT1$$FTR <- as.factor(DT1$$FTR)

DT1.rows <- nrow(DT1)
DT1.sample <- sample(DT1.rows, DT1.rows * 0.6)

DT1.train <- DT1[DT1.sample, ]
DT1.test <- DT1[-DT1.sample, ]

DT1.model <- C5.0(DT1.train[, -1], DT1.train$FTR, trails = 100) plot(DT1.model) summary(DT1.model) DT1.predict <- predict (DT1.model, DT1.test[, -1]) CrossTable( DT1.test$FTR,
DT1.predict,
prop.c = FALSE,
prop.r = FALSE,
prop.chisq = FALSE
)