When it comes to ensemble methods for classification tasks, Random Forest is a popular choice.
However, XGBoost’s implementation of Random Forest, XGBRFClassifier, offers significant speed improvements over scikit-learn’s RandomForestClassifier.
Let’s compare the training time of these two classifiers on a synthetic dataset.
from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
from xgboost import XGBRFClassifier
import time
# Generate a synthetic multiclass classification dataset
X, y = make_classification(n_samples=100000, n_classes=5, n_informative=10, random_state=42)
# Initialize the classifiers with comparable hyperparameters
xgbrf_clf = XGBRFClassifier(n_estimators=100, max_depth=10, random_state=42)
rf_clf = RandomForestClassifier(n_estimators=100, max_depth=10, random_state=42)
# Fit XGBRFClassifier and measure the training time
start_time = time.perf_counter()
xgbrf_clf.fit(X, y)
xgbrf_time = time.perf_counter() - start_time
print(f"XGBRFClassifier training time: {xgbrf_time:.2f} seconds")
# Fit RandomForestClassifier and measure the training time
start_time = time.perf_counter()
rf_clf.fit(X, y)
rf_time = time.perf_counter() - start_time
print(f"RandomForestClassifier training time: {rf_time:.2f} seconds")
In this example, we generate a synthetic multiclass classification dataset using scikit-learn’s make_classification function with 10,000 samples, 5 classes, and 10 informative features.
We then initialize XGBRFClassifier and RandomForestClassifier with identical hyperparameters:
n_estimators=100: The number of trees in the forest.max_depth=10: The maximum depth of each tree.random_state=42: For reproducibility.
Next, we fit each classifier on the dataset and measure the training time using the time module. The start_time is recorded before fitting the model, and the elapsed time is calculated after the fit completes.
Finally, we print the training times for both classifiers.
On running this code, you might see output similar to:
XGBRFClassifier training time: 12.80 seconds
RandomForestClassifier training time: 32.82 seconds
The actual times may vary depending on your hardware and system configuration, but you should observe that XGBRFClassifier trains faster than RandomForestClassifier.
XGBoost’s implementation of Random Forest leverages its optimized tree-building algorithms and parallel processing capabilities to achieve this speed-up. By using XGBRFClassifier, you can train Random Forest models more efficiently, allowing you to iterate faster and work with larger datasets.
Keep in mind that while XGBRFClassifier is faster, the resulting models may not always be identical to those produced by RandomForestClassifier due to differences in the underlying implementations. However, in most cases, the performance and accuracy of the two classifiers should be comparable.