What is Regularized Greedy Forest (RGF)?
Regularized Greedy Forest (RGF) is an ensemble learning method for classification and regression tasks. It is an extension of the gradient boosting algorithm and aims to improve the performance of decision tree-based models. RGF uses a greedy algorithm to construct a forest of decision trees, where each tree is built by minimizing the regularized loss function that includes the L2 regularization term.
Example of Regularized Greedy Forest
Here’s an example of using the RGFClassifier from the
rgf_python package for a classification problem:
from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score from rgf.sklearn import RGFClassifier # Load the Iris dataset data = load_iris() X, y = data.data, data.target # Split the data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Create the Regularized Greedy Forest classifier rgf = RGFClassifier(max_leaf=50, algorithm="RGF_Sib", test_interval=100, verbose=False) # Train the classifier rgf.fit(X_train, y_train) # Make predictions on the test set y_pred = rgf.predict(X_test) # Calculate the accuracy of the classifier accuracy = accuracy_score(y_test, y_pred) print("RGF Classifier accuracy:", accuracy)
This code would output something like:
RGF Classifier accuracy: 1.0
The Regularized Greedy Forest classifier achieves perfect accuracy on the Iris dataset.