Implementing Gradient Boosting Tree Algorithm in Python
Gradient Boosting Decision Tree (GBDT) is a common machine learning algorithm that builds a strong classifier by combining multiple weak classifiers. Compared with simple decision trees, GBDT has better performance and is more suitable for solving complex classification and regression problems.
We can use Python code to implement the GBDT algorithm. The main steps include: data preprocessing, model initialization, model training, and model prediction.
First, we need to prepare the dataset. Here we use the iris dataset that comes with the scikit-learn library for demonstration.
from sklearn import datasets
from sklearn.model_selection import train_test_split
# 加载数据集并划分训练集和测试集
iris = datasets.load_iris()
X_train, X_test, y_train,<