如何使用随机森林、决策树和XGBoost、svm分类器对数据进行交叉验证，并将每个分类器的得分保存到对应的列表中。具体来说，对于每个分类器，循环5次进行交叉验证，然后将得到的平均得分添加到对应的列表中。最后，将每个分类器的得分在同一张图表上进行绘制，方便比较它们的性能优劣。

以下是一个示例代码，展示如何使用sklearn库中的分类器对数据进行交叉验证，并将得分保存到列表中：

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.model_selection import KFold
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
from sklearn.svm import SVC

# 加载数据
iris = load_iris()
X = iris.data
y = iris.target

# 定义分类器
rfc = RandomForestClassifier()
dtc = DecisionTreeClassifier()
xgb = XGBClassifier()
svc = SVC()

# 定义交叉验证
kf = KFold(n_splits=5)

# 定义结果列表
rfc_scores = []
dtc_scores = []
xgb_scores = []
svc_scores = []

# 循环进行交叉验证
for train_index, test_index in kf.split(X):
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]
    
    # 随机森林
    rfc.fit(X_train, y_train)
    rfc_score = rfc.score(X_test, y_test)
    rfc_scores.append(rfc_score)
    
    # 决策树
    dtc.fit(X_train, y_train)
    dtc_score = dtc.score(X_test, y_test)
    dtc_scores.append(dtc_score)
    
    # XGBoost
    xgb.fit(X_train, y_train)
    xgb_score = xgb.score(X_test, y_test)
    xgb_scores.append(xgb_score)
    
    # SVM
    svc.fit(X_train, y_train)
    svc_score = svc.score(X_test, y_test)
    svc_scores.append(svc_score)

# 计算平均得分
rfc_mean_score = np.mean(rfc_scores)
dtc_mean_score = np.mean(dtc_scores)
xgb_mean_score = np.mean(xgb_scores)
svc_mean_score = np.mean(svc_scores)

# 绘制图表
labels = ['Random Forest', 'Decision Tree', 'XGBoost', 'SVM']
scores = [rfc_mean_score, dtc_mean_score, xgb_mean_score, svc_mean_score]
plt.bar(labels, scores)
plt.ylabel('Accuracy')
plt.ylim((0.8, 1.0))
plt.show()

这段代码将加载鸢尾花数据集，并使用随机森林、决策树、XGBoost和SVM分类器对数据进行交叉验证。它将每个分类器的得分保存到相应的列表中，并计算每个分类器的平均得分。最后，它将每个分类器的平均得分绘制在同一张图表上，以便进行比较。