使用鸢尾花数据集对随机森林、Xgboost、LightGBM 进行建模和预测代码

在Python中，我们可以使用scikit-learn库来处理鸢尾花数据集并训练随机森林、XGBoost和LightGBM模型。这里是一个简单的示例代码：

# 导入所需库
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score
import xgboost as xgb
from lightgbm import LGBMClassifier
from sklearn.ensemble import RandomForestClassifier

# 加载鸢尾花数据集
iris = load_iris()
X = iris.data
y = iris.target

# 数据预处理
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)

# 定义和训练模型
models = {
    'Random Forest': RandomForestClassifier(n_estimators=100, random_state=42),
    'XGBoost': xgb.XGBClassifier(objective='multi:softmax', num_class=3, n_estimators=100, random_state=42),
    'LightGBM': LGBMClassifier(num_leaves=31, learning_rate=0.1, n_estimators=100, random_state=42)
}

for name, model in models.items():
    print(f"Training {name}...")
    model.fit(X_train, y_train)
    
    # 预测
    y_pred = model.predict(X_test)
    
    # 评估
    accuracy = accuracy_score(y_test, y_pred)
    print(f"{name} Accuracy: {accuracy}")

# 结果展示

这个代码会分别训练随机森林、XGBoost和LightGBM，并在测试集上计算各自的准确率。请注意，实际应用中可能需要调整模型参数以获得更好的性能。