如何在H2OGradientBoostingEstimator中为交互变量提供参数输入？

import h2o
from h2o.estimators.gbm import H2OGradientBoostingEstimator

# 初始化 H2O 集群
h2o.init()

# 加载数据集
data = h2o.import_file("path_to_your_data.csv")

# 创建交互特征
data['feature1_feature2'] = data['feature1'] * data['feature2']

# 指定响应变量和特征
response = 'your_response_variable'
features = ['feature1', 'feature2', 'feature1_feature2'] + [col for col in data.columns if col not in ['your_response_variable', 'feature1', 'feature2']]

# 训练模型
model = H2OGradientBoostingEstimator()
model.train(x=features, y=response, training_frame=data)

import h2o
from h2o.automl import H2OAutoML

# 初始化 H2O 集群
h2o.init()

# 加载数据集
data = h2o.import_file("path_to_your_data.csv")

# 指定响应变量
response = 'your_response_variable'

# 运行 AutoML
aml = H2OAutoML(max_models=10, seed=1)
aml.train(x=data.columns, y=response, training_frame=data)

# 查看最佳模型
best_model = aml.leader