Python 决策树非常小，无法读取

我试图用我的信用卡客户数据集绘制一个决策树。我得到的图表太小，难以阅读。我如何解决这个问题？我是否犯过任何错误，或者如何改进此图表的可视化。 我已经附上了图像和代码的输出

我使用的代码：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import LabelEncoder

from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.tree import plot_tree


df=pd.read_csv(r'G:\\Edu\\My academics\\MSc in CS\\3rd sem\\Research\\Python files\\BC.csv')
##df = sns.load_dataset(r'G:\\Edu\\My academics\\MSc in CS\\3rd sem\\Research\\Python files\\BC.csv')
df.head()



df.head()
df.drop(['CLIENTNUM',
'Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1',
'Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2'],axis = 1, inplace = True)
print(df.columns)


sns.heatmap(df.corr())
plt.show()
df = pd.get_dummies(df, columns = ['Gender','Education_Level','Marital_Status','Income_Category','Card_Category'])
target = df['Attrition_Flag']

df1 = df.copy()
df1 = df1.drop('Attrition_Flag', axis =1)
X = df1

le = LabelEncoder()
target = le.fit_transform(target)
y = target
print(y)
X_train, X_test, y_train, y_test = train_test_split(X , y, test_size = 0.2, random_state = 42)
print("Training split input- ", X_train.shape)
print("Testing split input- ", X_test.shape)

from sklearn import tree
dtree = tree.DecisionTreeClassifier()
dtree.fit(X_train,y_train)

# Predicting the values of test data
y_pred = dtree.predict(X_test)
print("Classification report - \n", classification_report(y_test,y_pred))

cm = confusion_matrix(y_test, y_pred)
plt.figure(figsize=(5,5))
sns.heatmap(data=cm,linewidths=.5, annot=True,square = True,  cmap = 'Blues')
plt.ylabel('Actual label')
plt.xlabel('Predicted label')
all_sample_title = 'Accuracy Score: {0}'.format(dtree.score(X_test, y_test))
plt.title(all_sample_title, size = 15)
plt.show()
# Visualising the graph without the use of graphplt.figure(figsize = (25,25))
plt.figure(figsize=(12,12))
tree.plot_tree(dtree, fontsize=6)
plt.savefig('tree_high_dpi', dpi=100)

输出结果