When working with machine learning models in R, you may encounter different results depending on whether you use the xgboost package directly or through the caret package. This article explores why these differences occur and how to manage them to ensure consistent and reliable model performance.

Introduction to xgboost and Caret

xgboost is a powerful and efficient implementation of the gradient boosting algorithm. It is widely used for its performance and speed, especially in handling large datasets. The package allows for fine-tuned control over various parameters, making it a favorite among data scientists and machine learning practitioners.

caret (short for Classification And Regression Training) is a comprehensive package that provides a unified interface for training and tuning various machine learning models. It includes functionality for preprocessing, feature selection, model training, and evaluation, and supports a wide range of algorithms, including xgboost.

Why Results Might Differ

When comparing results between xgboost and caret, several factors can lead to differences:

• Hyperparameter Defaults: Different default values for hyperparameters can lead to variations in model performance. While xgboost provides its own defaults, caret might use different defaults or allow for additional preprocessing steps.
• Cross-Validation: The way cross-validation is implemented and the specific folds used can lead to different outcomes. caret allows for a more structured approach to cross-validation, whereas direct use of xgboost might involve manual setup.
• Data Preprocessing: caret includes extensive data preprocessing options (e.g., normalization, imputation) which might not be applied when using xgboost directly. This can significantly affect model performance and outcomes.
• Seed Setting: Setting a random seed ensures reproducibility, but if the seed is set differently or not set at all, results will vary.
• Metric Calculation: Different ways of calculating performance metrics can also lead to differences. For example, caret might use a different method for computing metrics during cross-validation compared to xgboost.

Example 1: Using xgboost model

Here is an example of training a model using xgboost directly:

# Load necessary libraries
library(xgboost)

# Load the iris dataset
data(iris)
iris_matrix <- model.matrix(Species ~ . - 1, data = iris)
labels <- as.numeric(iris$Species) - 1

# Set a seed for reproducibility
set.seed(123)

# Split data into training and testing sets
train_index <- sample(1:nrow(iris), 0.7 * nrow(iris))
train_data <- iris_matrix[train_index, ]
train_labels <- labels[train_index]
test_data <- iris_matrix[-train_index, ]
test_labels <- labels[-train_index]

# Convert data to xgb.DMatrix
dtrain <- xgb.DMatrix(data = train_data, label = train_labels)
dtest <- xgb.DMatrix(data = test_data, label = test_labels)

# Train the model
params <- list(objective = "multi:softprob", num_class = 3)
model_xgb <- xgb.train(params = params, data = dtrain, nrounds = 100)

# Make predictions
preds <- predict(model_xgb, newdata = dtest)
pred_labels <- max.col(matrix(preds, ncol = 3, byrow = TRUE)) - 1

# Evaluate performance
confusionMatrix <- table(pred_labels, test_labels)
print(confusionMatrix)

Output:

           test_labels
pred_labels  0  1  2
          0 14  0  0
          1  0 17  0
          2  0  1 13

Example 2: Using caret

Now, let’s see how to achieve the same using caret:

# Load necessary libraries
library(caret)
library(xgboost)

# Load the iris dataset
data(iris)

# Set a seed for reproducibility
set.seed(123)

# Define training control with cross-validation
train_control <- trainControl(method = "cv", number = 5)

# Train the model using caret
model_caret <- train(Species ~ ., data = iris, method = "xgbTree", 
                     trControl = train_control)

# Print the model summary
print(model_caret)

Output:

eXtreme Gradient Boosting 

150 samples
  4 predictor
  3 classes: 'setosa', 'versicolor', 'virginica' 

No pre-processing
Resampling: Cross-Validated (5 fold) 
Summary of sample sizes: 120, 120, 120, 120, 120 
Resampling results across tuning parameters:

  eta  max_depth  colsample_bytree  subsample  nrounds  Accuracy   Kappa
  0.3  1          0.6               0.50        50      0.9466667  0.92 
  0.3  1          0.6               0.50       100      0.9466667  0.92 
  0.3  1          0.6               0.50       150      0.9333333  0.90 ..................................................................

Tuning parameter 'gamma' was held constant at a value of 0
Tuning
 parameter 'min_child_weight' was held constant at a value of 1
Accuracy was used to select the optimal model using the largest value.
The final values used for the model were nrounds = 50, max_depth = 1, eta =
 0.3, gamma = 0, colsample_bytree = 0.6, min_child_weight = 1 and subsample = 0.5.

Conclusion

Different results between xgboost and caret can arise due to variations in hyperparameter defaults, cross-validation, data preprocessing, seed settings, and metric calculations. By carefully aligning these aspects, you can ensure more consistent and reliable model performance. Whether you choose to use xgboost directly for greater control or caret for its streamlined interface, understanding these factors will help you achieve the best results for your machine learning tasks in R.