请给我修改后的这份代码，使它的模型评价高于0.6 import pandas as pd from sklearn.neighbors import KNeighborsClassifier from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import train_test_split from sklearn.metrics import f1_score 读取训练集和测试集数据 data_hk = pd.read_csv("员工满意度_train.csv", engine='python') 填充缺失值 data_hk.fillna(0, inplace=True) data_hk = data_hk.drop(labels=['division'],axis=1) 将分类特征进行编码 encoder = LabelEncoder() data_hk['package'] = encoder.fit_transform(data_hk['package'].astype(str)) 划分训练集和验证集 X_train, X_test, y_train, y_test = train_test_split(data_hk.iloc[:, 0:-1], data_hk.iloc[:, -1], test_size=0.45, random_state=10) 模型训练 model = KNeighborsClassifier(n_neighbors=4) model.fit(X_train, y_train) 读取测试集数据 forecast_hk = pd.read_csv("员工满意度_test_nolabel.csv", engine='python') forecast_hk = forecast_hk.drop(labels=['division'],axis=1) forecast_hk.fillna(0, inplace=True) forecast_hk['package'] = encoder.transform(forecast_hk['package'].astype(str)) 在测试集上进行预测 y_predict = model.predict(forecast_hk) 将预测结果添加到测试集中 forecast_hk['salary'] = y_predict forecast_hk.to_csv("员工满意度_test_nolabel.csv", index=False) 在验证集上进行预测和评估 y_pred_test = model.predict(X_test) score = f1_score(y_test, y_pred_test, average='macro') print("模型评价（f1-score）：", score)

以下是修改后的代码，希望可以帮助提高模型评价的分数：

import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score

# 读取训练集和测试集数据
data_hk = pd.read_csv("员工满意度_train.csv", engine='python')

# 填充缺失值
data_hk.fillna(0, inplace=True)
data_hk = data_hk.drop(labels=['division'],axis=1)

# 将分类特征进行编码
encoder = LabelEncoder()
data_hk['package'] = encoder.fit_transform(data_hk['package'].astype(str))

# 划分训练集和验证集
X_train, X_test, y_train, y_test = train_test_split(data_hk.iloc[:, 0:-1], data_hk.iloc[:, -1],
                                                    test_size=0.2, random_state=10)  # 将验证集比例改为20%

# 模型训练
model = KNeighborsClassifier(n_neighbors=5)  # 调整n_neighbors为5
model.fit(X_train, y_train)

# 在验证集上进行预测和评估
y_pred_test = model.predict(X_test)
score = f1_score(y_test, y_pred_test, average='macro')
print("模型评价（f1-score）：", score)

# 读取测试集数据
forecast_hk = pd.read_csv("员工满意度_test_nolabel.csv", engine='python')
forecast_hk = forecast_hk.drop(labels=['division'],axis=1)
forecast_hk.fillna(0, inplace=True)
forecast_hk['package'] = encoder.transform(forecast_hk['package'].astype(str))

# 在测试集上进行预测
y_predict = model.predict(forecast_hk)

# 将预测结果添加到测试集中
forecast_hk['salary'] = y_predict
forecast_hk.to_csv("员工满意度_test_nolabel.csv", index=False)

这次的修改主要包括以下几点：

1. 调整了验证集的比例，将测试集大小改为原来的20%。这样可以增加训练集的样本数量，提高模型的泛化能力。

2. 将模型的参数n_neighbors调整为5。可以尝试不同的参数值，选择效果最好的。

请尝试运行修改后的代码，看看是否能够提高模型评价的分数。如果还有其他问题，请随时提问！