import pandas as pdfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.metrics import accuracy_score, confusion_matriximport seaborn as snsimport matplotlib.pyplot as plt# 读取数据data = pd.read_excel('data.xlsx')# 将数据分为训练集和验证集train_data = data.sample(frac=0.8, random_state=1)test_data = data.drop(train_data.index)# 定义特征变量和目标变量features = ['feature1', 'feature2', 'feature3']target = 'target'# 训练随机森林模型rf = RandomForestClassifier(n_estimators=100, random_state=1)rf.fit(train_data[features], train_data[target])# 在验证集上进行预测并计算精度和混淆矩阵pred = rf.predict(test_data[features])accuracy = accuracy_score(test_data[target], pred)confusion_mat = confusion_matrix(test_data[target], pred)print('Accuracy:', accuracy)print('Confusion matrix:')print(confusion_mat)# 输出混淆矩阵图片sns.heatmap(confusion_mat, annot=True)plt.show()# 读取新数据文件并预测结果new_data = pd.read_excel('new_data.xlsx')new_pred = rf.predict(new_data[features])new_data['predicted_target'] = new_prednew_data.to_excel('predicted_results.xlsx', index=False)改进代码并输出计算分类模型的准确率、召回率和F1值等指标

这里是改进后的代码，并输出计算分类模型的准确率、召回率和F1值等指标。

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

import seaborn as sns
import matplotlib.pyplot as plt

# 读取数据
data = pd.read_excel('data.xlsx')

# 将数据分为训练集和验证集
train_data = data.sample(frac=0.8, random_state=1)
test_data = data.drop(train_data.index)

# 定义特征变量和目标变量
features = ['feature1', 'feature2', 'feature3']
target = 'target'

# 训练随机森林模型
rf = RandomForestClassifier(n_estimators=100, random_state=1)
rf.fit(train_data[features], train_data[target])

# 在验证集上进行预测并计算精度、召回率和F1值等指标
pred = rf.predict(test_data[features])
accuracy = accuracy_score(test_data[target], pred)
confusion_mat = confusion_matrix(test_data[target], pred)
classification_rep = classification_report(test_data[target], pred)

print('Accuracy:', accuracy)
print('Confusion matrix:')
print(confusion_mat)
print('Classification report:')
print(classification_rep)

# 输出混淆矩阵图片
sns.heatmap(confusion_mat, annot=True)
plt.show()

# 读取新数据文件并预测结果
new_data = pd.read_excel('new_data.xlsx')
new_pred = rf.predict(new_data[features])
new_data['predicted_target'] = new_pred
new_data.to_excel('predicted_results.xlsx', index=False)

其中，`classification_report`函数可以计算模型的准确率、召回率和F1值等指标。