How to calculate the error rate for a decision tree with R?
To calculate the error rate for a decision tree in R, assuming the mean computing error rate on the sample used to fit the model, we can use printcp().
> library(rpart)
> fit <- rpart(Kyphosis ~ Age + Number + Start, data=kyphosis)
> printcp(fit)
Classification tree:
rpart(formula = Kyphosis ~ Age + Number + Start, data = kyphosis)
Variables actually used in tree construction:
[1] Age Start
Root node error: 17/81 = 0.20988
n= 81
CP nsplit rel error xerror xstd
1 0.176471 0 1.00000 1.00000 0.21559
2 0.019608 1 0.82353 0.82353 0.20018
3 0.010000 4 0.76471 0.82353 0.20018
The Root node error is used to compute two measures of predictive performance, when considering values displayed in the rel error column and xerror column.
It is also seen that it is more or less in agreement with classification accuracy from tree
> library(tree)
> summary(tree(Kyphosis ~ Age + Number + Start, data=kyphosis))
Classification tree:
tree(formula = Kyphosis ~ Age + Number + Start, data = kyphosis)
Number of terminal nodes: 10
Residual mean deviance: 0.5809 = 41.24 / 71
Misclassification error rate: 0.1235 = 10 / 81
Here Misclassification error rate is computed from the training sample.
Answer (1)
Calculating the error rate for a decision tree model in R involves evaluating the model's predictions against actual outcomes from a test dataset. Here’s a step-by-step guide on how to calculate the error rate using R:
Step 1: Load Required Libraries and Prepare Data
First, you need to load any necessary libraries and prepare your data. Here’s a simple example using the iris dataset included in R:
# Load necessary library (if not already loaded)library(rpart) # For decision tree modeling# Load example datasetdata(iris)Step 2: Build a Decision Tree Model
Next, build a decision tree model using the rpart package and a subset of the iris dataset:
# Split data into training and testing setsset.seed(123) # Set seed for reproducibilitytrain_index <- sample(1:nrow(iris), 100) # Example: 100 rows for trainingtrain_data <- iris[train_index, ]test_data <- iris[-train_index, ]# Build decision tree modeltree_model <- rpart(Species ~ ., data = train_data, method = "class")Step 3: Make Predictions
Use the trained model to make predictions on the test dataset:
# Predict on test datasetpredictions <- predict(tree_model, newdata = test_data, type = "class")Step 4: Calculate Error Rate
Calculate the error rate by comparing the predicted classes (predictions) with the actual classes (test_data$Species):
# Calculate error rateerror_rate <- mean(predictions != test_data$Species)Step 5: Interpret the Error Rate
The error_rate variable now contains the proportion of incorrect predictions made by the decision tree model on the test dataset. A lower error rate indicates better model performance.
Example Output
Here’s an example of how you might output and interpret the error rate:
# Print error ratecat("Error Rate:", error_rate, "
")# Print accuracyaccuracy <- 1 - error_ratecat("Accuracy:", accuracy, "
")Additional Considerations
Cross-Validation: For more reliable error rate estimation, consider using cross-validation techniques such as k-fold cross-validation.
Confusion Matrix: To get a more detailed understanding of prediction errors (e.g., type of errors), consider generating a confusion matrix using the caret package or confusionMatrix function in caret.
By following these steps, you can effectively calculate and interpret the error rate for a decision tree model in R, using standard functions and best practices in machine learning evaluation.