XGboost分类模型算法代码实现

XGBoost是一种GBDT（梯度提升树）算法，它具有高效性和准确性，被广泛应用于分类、回归和排序等领域。以下是XGBoost分类模型的算法代码实现：

1. 导入必要的库

import numpy as np
import pandas as pd
import xgboost as xgb
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

2. 加载数据集并进行划分

iris = load_iris()
X = iris.data
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test, label=y_test)

3. 设置XGBoost分类模型的参数

params = {
    'max_depth': 3,  # 树的最大深度
    'eta': 0.3,  # 学习率
    'objective': 'multi:softmax',  # 多分类问题，采用softmax目标函数
    'num_class': 3  # 类别数
}
num_round = 50  # 迭代次数

4. 训练模型并进行预测

model = xgb.train(params, dtrain, num_round)
y_pred = model.predict(dtest)

5. 计算准确率

accuracy = accuracy_score(y_test, y_pred)
print("Accuracy: %.2f%%" % (accuracy * 100.0))

完整代码如下：

import numpy as np
import pandas as pd
import xgboost as xgb
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

iris = load_iris()
X = iris.data
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test, label=y_test)

params = {
    'max_depth': 3,
    'eta': 0.3,
    'objective': 'multi:softmax',
    'num_class': 3
}
num_round = 50

model = xgb.train(params, dtrain, num_round)
y_pred = model.predict(dtest)

accuracy = accuracy_score(y_test, y_pred)
print("Accuracy: %.2f%%" % (accuracy * 100.0))