【实验项目1】 1.学习并调用RandomForestClassifier处理预测问题，并尝试用于真实数据集，并提供完整的python代码； 2.学习并调用RandomForestRegressor处理预测问题，并尝试用于真实数据集，并提供完整的python代码； 【实验项目2】 1.学习并调用ExtraTreesClassifier处理预测问题，并尝试用于真实数据集，并提供完整的python代码。 2.学习并调用ExtraTreesRegressor处理预测问题，并尝试用于真实数据集，并提供完整的python代码。

【实验项目1】

1. 使用RandomForestClassifier进行分类

from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# 加载鸢尾花数据集
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)

# 构建随机森林分类器
rfc = RandomForestClassifier(n_estimators=100, random_state=42)
rfc.fit(X_train, y_train)

# 预测测试集
y_pred = rfc.predict(X_test)

# 计算准确率
accuracy = (y_pred == y_test).sum() / len(y_test)
print("Accuracy:", accuracy)

2. 使用RandomForestRegressor进行回归

from sklearn.ensemble import RandomForestRegressor
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# 加载波士顿房价数据集
boston = load_boston()
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target, test_size=0.2, random_state=42)

# 构建随机森林回归器
rfr = RandomForestRegressor(n_estimators=100, random_state=42)
rfr.fit(X_train, y_train)

# 预测测试集
y_pred = rfr.predict(X_test)

# 计算均方误差
mse = mean_squared_error(y_test, y_pred)
print("MSE:", mse)

【实验项目2】

1. 使用ExtraTreesClassifier进行分类

from sklearn.ensemble import ExtraTreesClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# 加载鸢尾花数据集
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)

# 构建ExtraTrees分类器
etc = ExtraTreesClassifier(n_estimators=100, random_state=42)
etc.fit(X_train, y_train)

# 预测测试集
y_pred = etc.predict(X_test)

# 计算准确率
accuracy = (y_pred == y_test).sum() / len(y_test)
print("Accuracy:", accuracy)

2. 使用ExtraTreesRegressor进行回归

from sklearn.ensemble import ExtraTreesRegressor
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# 加载波士顿房价数据集
boston = load_boston()
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target, test_size=0.2, random_state=42)

# 构建ExtraTrees回归器
etr = ExtraTreesRegressor(n_estimators=100, random_state=42)
etr.fit(X_train, y_train)

# 预测测试集
y_pred = etr.predict(X_test)

# 计算均方误差
mse = mean_squared_error(y_test, y_pred)
print("MSE:", mse)