构造完整的random_forecasting .py程序代码。 运行带有随机森林分类器的代码，在输入参数中使用rf标志，通过运行以下命令: $ python random_forests.py——分类器类型rf 将两幅图(如果成功)保存到以下表格中，以便提交。 现在，通过在输入参数中使用erf标志，使用极其随机的森林分类器运行代码。执行如下命令: $ python random_forests.py——分类器类型erf

我可以回答这个问题。以下是构造完整的random_forecasting.py程序代码：

import pandas as pd
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split

# Load data
data = pd.read_csv('data.csv')

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(data.drop('target', axis=1), data['target'], test_size=0.2, random_state=42)

# Define classifier type based on input flag
if sys.argv[1] == 'rf':
    clf = RandomForestClassifier()
elif sys.argv[1] == 'erf':
    clf = ExtraTreesClassifier()

# Train classifier
clf.fit(X_train, y_train)

# Make predictions on test set
y_pred = clf.predict(X_test)

# Calculate accuracy score
accuracy = accuracy_score(y_test, y_pred)

# Save accuracy score to file
with open('accuracy.txt', 'w') as f:
    f.write(str(accuracy))

# Save feature importances to file
importances = clf.feature_importances_
with open('importances.txt', 'w') as f:
    for i in range(len(importances)):
        f.write(str(importances[i]) + '\n')

如果运行时使用了rf标志，则会使用随机森林分类器。如果使用了erf标志，则会使用极其随机的森林分类器。程序会将准确度和特征重要性保存到文件中。