Calculating prediction accuracy of a tree using rpart's predict method

Question

I have constructed a decision tree using rpart for a dataset. I have then divided the data into 2 parts - a training dataset and a test dataset. A tree has been constructed for the dataset using the training data. I want to calculate the accuracy of the predictions based on the model that was created. My code is shown below: ``` library(rpart) #reading the data data = read.table("source") names(data) <- c("a", "b", "c", "d", "class") #generating test and train data - Data selected randomly with a 80/20 split trainIndex <- sample(1:nrow(x), 0.8 * nrow(x)) train <- data[trainIndex,] test <- data[-trainIndex,] #tree construction based on information gain tree = rpart(class ~ a + b + c + d, data = train, method = 'class', parms = list(split = "information")) ``` I now want to calculate the accuracy of the predictions generated by the model by comparing the results with the actual values train and test data however I am facing an error while doing so. My code is shown below: ``` t_pred = p…

Accepted Answer

Try calculating the confusion matrix first:

```
confMat <- table(test$class,t_pred)
```

Now you can calculate the accuracy by dividing the sum diagonal of the matrix - which are the correct predictions - by the total sum of the matrix:

```
accuracy <- sum(diag(confMat))/sum(confMat)
```

Calculating prediction accuracy of a tree using rpart's predict method

Solution

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