针对问题二该如何解决最大次数的组合问题，并且建模预测。写Python代码

针对问题二，我们需要根据提供的实测训练集中的测试基本信息，尤其是节点间RSSI信息和门限信息，结合对AP发送机会的分析，对测试中AP发送数据选用最多次数的（MCS, NSS）进行建模，并通过测试集 `test_set_2_2ap` 和 `test_set_2_3ap` 预测（MCS, NSS）。

这里我们提供一个简化版的Python代码框架来实现上述任务：

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.metrics import accuracy_score

# 加载数据
train_data = pd.read_csv('training_set.csv')
test_data_2ap = pd.read_csv('test_set_2_2ap.csv')
test_data_3ap = pd.read_csv('test_set_2_3ap.csv')

# 特征工程 - 选择影响性强的特征
features = ['eirp', 'ap_from_ap_x_max_ant_rssi', 'pd', 'ed', 'nav']
X_train = train_data[features]
y_train_mcs = train_data['mcs']  # 假设'MCS'列名为'mcs'
y_train_nss = train_data['nss']  # 假设'NSS'列名为'nss'

# 数据预处理 - 标准化
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)

# 划分训练集和验证集
X_train_final, X_val, y_train_final_mcs, y_val_mcs, y_train_final_nss, y_val_nss = train_test_split(
    X_train_scaled, y_train_mcs, y_train_nss, test_size=0.2, random_state=42)

# 构建模型 - 使用线性回归作为示例模型
model_mcs = LinearRegression()
model_nss = LinearRegression()

# 训练模型
model_mcs.fit(X_train_final, y_train_final_mcs)
model_nss.fit(X_train_final, y_train_final_nss)

# 验证模型
y_pred_mcs = model_mcs.predict(X_val)
y_pred_nss = model_nss.predict(X_val)

# 处理预测结果为最接近的整数值
y_pred_mcs_rounded = [round(x) for x in y_pred_mcs]
y_pred_nss_rounded = [round(x) for x in y_pred_nss]

# 输出准确性分数
print(f"Accuracy Score for MCS: {accuracy_score(y_val_mcs, y_pred_mcs_rounded)}")
print(f"Accuracy Score for NSS: {accuracy_score(y_val_nss, y_pred_nss_rounded)}")

# 对测试集进行预测
test_data_2ap_scaled = scaler.transform(test_data_2ap[features])
test_data_3ap_scaled = scaler.transform(test_data_3ap[features])

pred_mcs_2ap = model_mcs.predict(test_data_2ap_scaled)
pred_nss_2ap = model_nss.predict(test_data_2ap_scaled)

pred_mcs_3ap = model_mcs.predict(test_data_3ap_scaled)
pred_nss_3ap = model_nss.predict(test_data_3ap_scaled)

# 将预测结果转换为整数
pred_mcs_2ap_rounded = [round(x) for x in pred_mcs_2ap]
pred_nss_2ap_rounded = [round(x) for x in pred_nss_2ap]

pred_mcs_3ap_rounded = [round(x) for x in pred_mcs_3ap]
pred_nss_3ap_rounded = [round(x) for x in pred_nss_3ap]

# 结果输出
results_2ap = {'mcs': pred_mcs_2ap_rounded, 'nss': pred_nss_2ap_rounded}
results_3ap = {'mcs': pred_mcs_3ap_rounded, 'nss': pred_nss_3ap_rounded}

print("Predictions for Test Set with 2 APs:", results_2ap)
print("Predictions for Test Set with 3 APs:", results_3ap)

请注意，以上代码仅为示意性的实现，并未考虑实际情况中的数据格式和细节，您需要根据具体的CSV文件结构和需求进行调整。此外，线性回归只是一个简单的示例模型，您可以尝试其他的回归模型或更高级的机器学习算法来改进预测效果。