调整lightgbm的num_iterations的代码

以下是调整lightgbm的num_iterations的示例代码，其中将num_iterations设置为1000：

import lightgbm as lgb
import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split

# 加载数据集
data = load_breast_cancer()
X = data.data
y = data.target

# 将数据集拆分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 定义模型参数
params = {
    'objective': 'binary',
    'metric': 'binary_logloss',
    'num_iterations': 1000,  # 设置num_iterations为1000
    'num_leaves': 31,
    'learning_rate': 0.05,
    'feature_fraction': 0.9,
    'bagging_fraction': 0.8,
    'bagging_freq': 5,
    'verbose': -1
}

# 训练模型
train_data = lgb.Dataset(X_train, label=y_train)
eval_data = lgb.Dataset(X_test, label=y_test)
model = lgb.train(params, train_data, valid_sets=[train_data, eval_data], early_stopping_rounds=100, verbose_eval=False)

# 在测试集上进行预测
y_pred = model.predict(X_test)
y_pred[y_pred >= 0.5] = 1
y_pred[y_pred < 0.5] = 0

# 计算准确率
accuracy = np.mean(y_pred == y_test)
print('Accuracy:', accuracy)

在上面的代码中，我们将num_iterations设置为1000，并在训练模型时将其传递给params字典。