请给出完整的代码：参照“Understanding the decision tree structure”，对load_wine数据集进行分类（调参），并使用random forest对load_wine数据集进行分类（调参）。

首先，我们需要导入所需的库，如`sklearn`, `pandas`, 和 `numpy`，然后加载`load_wine`数据集。这个数据集通常位于`sklearn.datasets`模块中。接下来，我们将使用决策树和随机森林进行分类，并对它们的超参数进行调整。

# 导入所需库
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
import pandas as pd
import numpy as np

# 加载数据集
wine = load_wine()
X = wine.data
y = wine.target

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# 定义决策树模型和参数网格
dt_params = {
    'criterion': ['gini', 'entropy'],
    'max_depth': range(1, 11),
    'min_samples_split': [2, 5, 10],
}

# 决策树分类器
dt_clf = DecisionTreeClassifier()

# 使用GridSearchCV进行参数搜索
dt_grid_search = GridSearchCV(dt_clf, dt_params, cv=5, scoring='accuracy')
dt_grid_search.fit(X_train, y_train)

# 打印最佳参数及其准确率
best_dt_params = dt_grid_search.best_params_
print(f"Best Decision Tree parameters: {best_dt_params}")
dt_best_model = dt_grid_search.best_estimator_
dt_accuracy = accuracy_score(y_test, dt_best_model.predict(X_test))
print(f"Decision Tree Accuracy: {dt_accuracy}")

# 定义随机森林模型和参数网格
rf_params = {
    'n_estimators': [50, 100, 200],
    'max_depth': range(1, 11),
    'min_samples_split': [2, 5, 10],
    'max_features': ['auto', 'sqrt', 'log2']
}

# 随机森林分类器
rf_clf = RandomForestClassifier()

# 进行参数搜索
rf_grid_search = GridSearchCV(rf_clf, rf_params, cv=5, scoring='accuracy')
rf_grid_search.fit(X_train, y_train)

# 打印最佳参数及其准确率
best_rf_params = rf_grid_search.best_params_
print(f"Best Random Forest parameters: {best_rf_params}")
rf_best_model = rf_grid_search.best_estimator_
rf_accuracy = accuracy_score(y_test, rf_best_model.predict(X_test))
print(f"Random Forest Accuracy: {rf_accuracy}")